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1.
  • Aibinu, A.M., et al. (författare)
  • Vascular intersection detection in retina fundus images using a new hybrid approach
  • 2010
  • Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 40:1, s. 81-89
  • Tidskriftsartikel (refereegranskat)abstract
    • The use of vascular intersection aberration as one of the signs when monitoring and diagnosing diabetic retinopathy from retina fundus images (FIs) has been widely reported in the literature. In this paper, a new hybrid approach called the combined cross-point number (CCN) method able to detect the vascular bifurcation and intersection points in FIs is proposed. The CCN method makes use of two vascular intersection detection techniques, namely the modified cross-point number (MCN) method and the simple cross-point number (SCN) method. Our proposed approach was tested on images obtained from two different and publicly available fundus image databases. The results show a very high precision, accuracy, sensitivity and low false rate in detecting both bifurcation and crossover points compared with both the MCN and the SCN methods.
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2.
  • Ali, Subhan, et al. (författare)
  • The enlightening role of explainable artificial intelligence in medical & healthcare domains : A systematic literature review
  • 2023
  • Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 166
  • Tidskriftsartikel (refereegranskat)abstract
    • In domains such as medical and healthcare, the interpretability and explainability of machine learning and artificial intelligence systems are crucial for building trust in their results. Errors caused by these systems, such as incorrect diagnoses or treatments, can have severe and even life-threatening consequences for patients. To address this issue, Explainable Artificial Intelligence (XAI) has emerged as a popular area of research, focused on understanding the black-box nature of complex and hard-to-interpret machine learning models. While humans can increase the accuracy of these models through technical expertise, understanding how these models actually function during training can be difficult or even impossible. XAI algorithms such as Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) can provide explanations for these models, improving trust in their predictions by providing feature importance and increasing confidence in the systems. Many articles have been published that propose solutions to medical problems by using machine learning models alongside XAI algorithms to provide interpretability and explainability. In our study, we identified 454 articles published from 2018- 2022 and analyzed 93 of them to explore the use of these techniques in the medical domain.
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3.
  • Ariane, Mostapha, et al. (författare)
  • Discrete multi-physics simulations of diffusive and convective mass transfer in boundary layers containing motile cilia in lungs
  • 2018
  • Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 95, s. 34-42
  • Tidskriftsartikel (refereegranskat)abstract
    • In this paper, the mass transfer coefficient (permeability) of boundary layers containing motile cilia is investigated by means of discrete multi-physics. The idea is to understand the main mechanisms of mass transport occurring in a ciliated-layer; one specific application being inhaled drugs in the respiratory epithelium. The effect of drug diffusivity, cilia beat frequency and cilia flexibility is studied. Our results show the existence of three mass transfer regimes. A low frequency regime, which we called shielding regime, where the presence of the cilia hinders mass transport; an intermediate frequency regime, which we have called diffusive regime, where diffusion is the controlling mechanism; and a high frequency regime, which we have called convective regime, where the degree of bending of the cilia seems to be the most important factor controlling mass transfer in the ciliated-layer. Since the flexibility of the cilia and the frequency of the beat changes with age and health conditions, the knowledge of these three regimes allows prediction of how mass transfer varies with these factors.
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7.
  • Buddenkotte, Thomas, et al. (författare)
  • Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation
  • 2023
  • Ingår i: Computers in Biology and Medicine. - : Elsevier Ltd. - 0010-4825 .- 1879-0534. ; 163
  • Tidskriftsartikel (refereegranskat)abstract
    • Uncertainty quantification in automated image analysis is highly desired in many applications. Typically, machine learning models in classification or segmentation are only developed to provide binary answers; however, quantifying the uncertainty of the models can play a critical role for example in active learning or machine human interaction. Uncertainty quantification is especially difficult when using deep learning-based models, which are the state-of-the-art in many imaging applications. The current uncertainty quantification approaches do not scale well in high-dimensional real-world problems. Scalable solutions often rely on classical techniques, such as dropout, during inference or training ensembles of identical models with different random seeds to obtain a posterior distribution. In this paper, we present the following contributions. First, we show that the classical approaches fail to approximate the classification probability. Second, we propose a scalable and intuitive framework for uncertainty quantification in medical image segmentation that yields measurements that approximate the classification probability. Third, we suggest the usage of k-fold cross-validation to overcome the need for held out calibration data. Lastly, we motivate the adoption of our method in active learning, creating pseudo-labels to learn from unlabeled images and human–machine collaboration.
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8.
  • Chelebian, Eduard, et al. (författare)
  • DEPICTER : Deep representation clustering for histology annotation
  • 2024
  • Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 170
  • Tidskriftsartikel (refereegranskat)abstract
    • Automatic segmentation of histopathology whole -slide images (WSI) usually involves supervised training of deep learning models with pixel -level labels to classify each pixel of the WSI into tissue regions such as benign or cancerous. However, fully supervised segmentation requires large-scale data manually annotated by experts, which can be expensive and time-consuming to obtain. Non -fully supervised methods, ranging from semi -supervised to unsupervised, have been proposed to address this issue and have been successful in WSI segmentation tasks. But these methods have mainly been focused on technical advancements in algorithmic performance rather than on the development of practical tools that could be used by pathologists or researchers in real -world scenarios. In contrast, we present DEPICTER (Deep rEPresentatIon ClusTERing), an interactive segmentation tool for histopathology annotation that produces a patch -wise dense segmentation map at WSI level. The interactive nature of DEPICTER leverages self- and semi -supervised learning approaches to allow the user to participate in the segmentation producing reliable results while reducing the workload. DEPICTER consists of three steps: first, a pretrained model is used to compute embeddings from image patches. Next, the user selects a number of benign and cancerous patches from the multi -resolution image. Finally, guided by the deep representations, label propagation is achieved using our novel seeded iterative clustering method or by directly interacting with the embedding space via feature space gating. We report both real-time interaction results with three pathologists and evaluate the performance on three public cancer classification dataset benchmarks through simulations. The code and demos of DEPICTER are publicly available at https://github.com/eduardchelebian/depicter.
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9.
  • Chen, Ye, et al. (författare)
  • A global learning with local preservation method for microarray data imputation
  • 2016
  • Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 77, s. 76-89
  • Tidskriftsartikel (refereegranskat)abstract
    • Microarray data suffer from missing values for various reasons, including insufficient resolution, image noise, and experimental errors. Because missing values can hinder downstream analysis steps that require complete data as input, it is crucial to be able to estimate the missing values. In this study, we propose a Global Learning with Local Preservation method (GL2P) for imputation of missing values in microarray data. GL2P consists of two components: a local similarity measurement module and a global weighted imputation module. The former uses a local structure preservation scheme to exploit as much information as possible from the observable data, and the latter is responsible for estimating the missing values of a target gene by considering all of its neighbors rather than a subset of them. Furthermore, GL2P imputes the missing values in ascending order according to the rate of missing data for each target gene to fully utilize previously estimated values. To validate the proposed method, we conducted extensive experiments on six benchmarked microarray datasets. We compared GL2P with eight state-of-the-art imputation methods in terms of four performance metrics. The experimental results indicate that GL2P outperforms its competitors in terms of imputation accuracy and better preserves the structure of differentially expressed genes. In addition, GL2P is less sensitive to the number of neighbors than other local learning-based imputation. methods.
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10.
  • De Silva, Kushan, et al. (författare)
  • Clinical notes as prognostic markers of mortality associated with diabetes mellitus following critical care : A retrospective cohort analysis using machine learning and unstructured big data
  • 2021
  • Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 132
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Clinical notes are ubiquitous resources offering potential value in optimizing critical care via data mining technologies. Objective: To determine the predictive value of clinical notes as prognostic markers of 1-year all-cause mortality among people with diabetes following critical care. Materials and methods: Mortality of diabetes patients were predicted using three cohorts of clinical text in a critical care database, written by physicians (n = 45253), nurses (159027), and both (n = 204280). Natural language processing was used to pre-process text documents and LASSO-regularized logistic regression models were trained and tested. Confusion matrix metrics of each model were calculated and AUROC estimates between models were compared. All predictive words and corresponding coefficients were extracted. Outcome probability associated with each text document was estimated. Results: Models built on clinical text of physicians, nurses, and the combined cohort predicted mortality with AUROC of 0.996, 0.893, and 0.922, respectively. Predictive performance of the models significantly differed from one another whereas inter-rater reliability ranged from substantial to almost perfect across them. Number of predictive words with non-zero coefficients were 3994, 8159, and 10579, respectively, in the models of physicians, nurses, and the combined cohort. Physicians & rsquo; and nursing notes, both individually and when combined, strongly predicted 1-year all-cause mortality among people with diabetes following critical care. Conclusion: Clinical notes of physicians and nurses are strong and novel prognostic markers of diabetes-associated mortality in critical care, offering potentially generalizable and scalable applications. Clinical text-derived personalized risk estimates of prognostic outcomes such as mortality could be used to optimize patient care.
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11.
  • Fairley, Jacqueline A., et al. (författare)
  • Wavelet analysis for detection of phasic electromyographic activity in sleep : of mother wavelet and dimensionality reduction
  • 2014
  • Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 48:1, s. 77-84
  • Tidskriftsartikel (refereegranskat)abstract
    • Phasic electromyographic (EMG) activity during sleep is characterized by brief muscle twitches (duration 100-500. ms, amplitude four times background activity). High rates of such activity may have clinical relevance. This paper presents wavelet (WT) analyses to detect phasic EMG, examining both Symlet and Daubechies approaches. Feature extraction included 1. s epoch processing with 24 WT-based features and dimensionality reduction involved comparing two techniques: principal component analysis and a feature/variable selection algorithm. Classification was conducted using a linear classifier. Valid automated detection was obtained in comparison to expert human judgment with high (>90%) classification performance for 11/12 datasets
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12.
  • Faryna, Khrystyna, et al. (författare)
  • Automatic data augmentation to improve generalization of deep learning in H&E stained histopathology
  • 2024
  • Ingår i: Computers in Biology and Medicine. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0010-4825 .- 1879-0534. ; 170
  • Tidskriftsartikel (refereegranskat)abstract
    • In histopathology practice, scanners, tissue processing, staining, and image acquisition protocols vary from center to center, resulting in subtle variations in images. Vanilla convolutional neural networks are sensitive to such domain shifts. Data augmentation is a popular way to improve domain generalization. Currently, state-of-the-art domain generalization in computational pathology is achieved using a manually curated set of augmentation transforms. However, manual tuning of augmentation parameters is time-consuming and can lead to sub-optimal generalization performance. Meta-learning frameworks can provide efficient ways to find optimal training hyper-parameters, including data augmentation. In this study, we hypothesize that an automated search of augmentation hyper-parameters can provide superior generalization performance and reduce experimental optimization time. We select four state-of-theart automatic augmentation methods from general computer vision and investigate their capacity to improve domain generalization in histopathology. We analyze their performance on data from 25 centers across two different tasks: tumor metastasis detection in lymph nodes and breast cancer tissue type classification. On tumor metastasis detection, most automatic augmentation methods achieve comparable performance to state-of-the-art manual augmentation. On breast cancer tissue type classification, the leading automatic augmentation method significantly outperforms state-of-the-art manual data augmentation.
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13.
  • Gelzinis, Adas, et al. (författare)
  • Automatic detection and morphological delineation of bacteriophages in electron microscopy images
  • 2015
  • Ingår i: Computers in Biology and Medicine. - Kidlington : Pergamon Press. - 0010-4825 .- 1879-0534. ; 64, s. 101-116
  • Tidskriftsartikel (refereegranskat)abstract
    • Automatic detection, recognition and geometric characterization of bacteriophages in electron microscopy images was the main objective of this work. A novel technique, combining phase congruency-based image enhancement, Hough transform-, Radon transform- and open active contours with free boundary conditions-based object detection was developed to detect and recognize the bacteriophages associated with infection and lysis of cyanobacteria Aphanizomenon flos-aquae. A random forest classifier designed to recognize phage capsids provided higher than 99% accuracy, while measurable phage tails were detected and associated with a correct capsid with 81.35% accuracy. Automatically derived morphometric measurements of phage capsids and tails exhibited lower variability than the ones obtained manually. The technique allows performing precise and accurate quantitative (e.g. abundance estimation) and qualitative (e.g. diversity and capsid size) measurements for studying the interactions between host population and different phages that infect the same host. © 2015 Elsevier Ltd.
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14.
  • Gradolewski, Dawid, 1988-, et al. (författare)
  • Wavelet-based denoising method for real phonocardiography signal recorded by mobile devices in noisy environment
  • 2014
  • Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 52, s. 119-129
  • Tidskriftsartikel (refereegranskat)abstract
    • The main obstacle in development of intelligent autodiagnosis medical systems based on the analysis of phonocardiography (PCG) signals is noise. The noise can be caused by digestive and respiration sounds, movements or even signals from the surrounding environment and it is characterized by wide frequency and intensity spectrum. This spectrum overlaps the heart tones spectrum, which makes the problem of PCG signal filtrating complex. The most common method for filtering such signals are wavelet denoising algorithms. In previous studies, in order to determine the optimum wavelet denoising parameters the disturbances were simulated by Gaussian white noise. However, this paper shows that this noise has a variable character. Therefore, the purpose of this paper is adaptation of a wavelet denoising algorithm for the filtration of real PCG signal disturbances from signals recorded by a mobile devices in a noisy environment. The best results were obtained for Coif 5 wavelet at the 10th decomposition level with the use of a minimaxi threshold selection algorithm and mln rescaling function. The performance of the algorithm was tested on four pathological heart sounds: early systolic murmur, ejection click, late systolic murmur and pansystolic murmur.
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15.
  • Guarrasi, Valerio, et al. (författare)
  • Multi-objective optimization determines when, which and how to fuse deep networks : an application to predict COVID-19 outcomes
  • 2023
  • Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 154
  • Tidskriftsartikel (refereegranskat)abstract
    • The COVID-19 pandemic has caused millions of cases and deaths and the AI-related scientific community, after being involved with detecting COVID-19 signs in medical images, has been now directing the efforts towards the development of methods that can predict the progression of the disease. This task is multimodal by its very nature and, recently, baseline results achieved on the publicly available AIforCOVID dataset have shown that chest X-ray scans and clinical information are useful to identify patients at risk of severe outcomes. While deep learning has shown superior performance in several medical fields, in most of the cases it considers unimodal data only. In this respect, when, which and how to fuse the different modalities is an open challenge in multimodal deep learning. To cope with these three questions here we present a novel approach optimizing the setup of a multimodal end-to-end model. It exploits Pareto multi-objective optimization working with a performance metric and the diversity score of multiple candidate unimodal neural networks to be fused. We test our method on the AIforCOVID dataset, attaining state-of-the-art results, not only outperforming the baseline performance but also being robust to external validation. Moreover, exploiting XAI algorithms we figure out a hierarchy among the modalities and we extract the features’ intra-modality importance, enriching the trust on the predictions made by the model.
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16.
  • Guo, Xiaoyi, et al. (författare)
  • Random Fourier features-based sparse representation classifier for identifying DNA-binding proteins
  • 2022
  • Ingår i: Computers in Biology and Medicine. - London : Elsevier. - 0010-4825 .- 1879-0534. ; 151
  • Tidskriftsartikel (refereegranskat)abstract
    • DNA-binding proteins (DBPs) protect DNA from nuclease hydrolysis, inhibit the action of RNA polymerase,prevents replication and transcription from occurring simultaneously on a piece of DNA. Most of theconventional methods for detecting DBPs are biochemical methods, but the time cost is high. In recent years,a variety of machine learning-based methods that have been used on a large scale for large-scale screeningof DBPs. To improve the prediction performance of DBPs, we propose a random Fourier features-based sparserepresentation classifier (RFF-SRC), which randomly map the features into a high-dimensional space to solvenonlinear classification problems. And ?2,1-matrix norm is introduced to get sparse solution of model. Toevaluate performance, our model is tested on several benchmark data sets of DBPs and 8 UCI data sets. RFF-SRCachieves better performance in experimental results. © 2022 Elsevier Ltd. 
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17.
  • Hagberg, Eva, et al. (författare)
  • Semi-supervised learning with natural language processing for right ventricle classification in echocardiography—a scalable approach
  • 2022
  • Ingår i: Computers in Biology and Medicine. - : Elsevier BV. - 0010-4825 .- 1879-0534. ; 143
  • Tidskriftsartikel (refereegranskat)abstract
    • We created a deep learning model, trained on text classified by natural language processing (NLP), to assess right ventricular (RV) size and function from echocardiographic images. We included 12,684 examinations with corresponding written reports for text classification. After manual annotation of 1489 reports, we trained an NLP model to classify the remaining 10,651 reports. A view classifier was developed to select the 4-chamber or RV-focused view from an echocardiographic examination (n = 539). The final models were two image classification models trained on the predicted labels from the combined manual annotation and NLP models and the corresponding echocardiographic view to assess RV function (training set n = 11,008) and size (training set n = 9951. The text classifier identified impaired RV function with 99% sensitivity and 98% specificity and RV enlargement with 98% sensitivity and 98% specificity. The view classification model identified the 4-chamber view with 92% accuracy and the RV-focused view with 73% accuracy. The image classification models identified impaired RV function with 93% sensitivity and 72% specificity and an enlarged RV with 80% sensitivity and 85% specificity; agreement with the written reports was substantial (both κ = 0.65). Our findings show that models for automatic image assessment can be trained to classify RV size and function by using model-annotated data from written echocardiography reports. This pipeline for auto-annotation of the echocardiographic images, using a NLP model with medical reports as input, can be used to train an image-assessment model without manual annotation of images and enables fast and inexpensive expansion of the training dataset when needed. © 2022
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18.
  • Hassan, Sk Sarif, et al. (författare)
  • A unique view of SARS-CoV-2 through the lens of ORF8 protein
  • 2021
  • Ingår i: Computers in Biology and Medicine. - : Elsevier BV. - 0010-4825 .- 1879-0534. ; 133
  • Tidskriftsartikel (refereegranskat)abstract
    • Immune evasion is one of the unique characteristics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) attributed to its ORF8 protein. This protein modulates the adaptive host immunity through down regulation of MHC-1 (Major Histocompatibility Complex) molecules and innate immune responses by surpassing the host's interferon-mediated antiviral response. To understand the host's immune perspective in reference to the ORF8 protein, a comprehensive study of the ORF8 protein and mutations possessed by it have been performed. Chemical and structural properties of ORF8 proteins from different hosts, such as human, bat, and pangolin, suggest that the ORF8 of SARS-CoV-2 is much closer to ORF8 of Bat RaTG13-CoV than to that of Pangolin-CoV. Eighty-seven mutations across unique variants of ORF8 in SARS-CoV-2 can be grouped into four classes based on their predicted effects (Hussain et al., 2021) [1]. Based on the geo-locations and timescale of sample collection, a possible flow of mutations was built. Furthermore, conclusive flows of amalgamation of mutations were found upon sequence similarity analyses and consideration of the amino acid conservation phylogenies. Therefore, this study seeks to highlight the uniqueness of the rapidly evolving SARS-CoV-2 through the ORF8.
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19.
  • Ibrahim, Mahmoud A. A., et al. (författare)
  • In silico drug discovery of major metabolites from spices as SARS-CoV-2 main protease inhibitors
  • 2020
  • Ingår i: Computers in Biology and Medicine. - : Elsevier BV. - 0010-4825 .- 1879-0534. ; 126
  • Tidskriftsartikel (refereegranskat)abstract
    • Coronavirus Disease 2019 (COVID-19) is an infectious illness caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), originally identified in Wuhan, China (December 2019) and has since expanded into a pandemic. Here, we investigate metabolites present in several common spices as possible inhibitors of COVID-19. Specifically, 32 compounds isolated from 14 cooking seasonings were examined as inhibitors for SARS-CoV-2 main protease (MPrn), which is required for viral multiplication. Using a drug discovery approach to identify possible antiviral leads, in silico molecular docking studies were performed. Docking calculations revealed a high potency of salvianolic acid A and curcumin as MPr inhibitors with binding energies of 9.7 and 9.2 kcal/mol, respectively. Binding mode analysis demonstrated the ability of salvianolic acid A and curcumin to form nine and six hydrogen bonds, respectively with amino acids proximal to MPr 's active site. Stabilities and binding affinities of the two identified natural spices were calculated over 40 ns molecular dynamics simulations and compared to an antiviral protease inhibitor (lopinavir). Molecular mechanics-generalized Born surface area energy calculations revealed greater salvianolic acid A affinity for the enzyme over curcumin and lopinavir with energies of 44.8, 34.2 and 34.8 kcal/mol, respectively. Using a STRING database, protein-protein interactions were identified for salvianolic acid A included the biochemical signaling genes ACE, MAPK14 and ESR1; and for curcumin, EGFR and TNF. This study establishes salvianolic acid A as an in silico natural product inhibitor against the SARS-CoV-2 main protease and provides a promising inhibitor lead for in vitro enzyme testing.
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20.
  • Jiang, Linfeng, et al. (författare)
  • RMAU-Net : Residual Multi-Scale Attention U-Net For liver and tumor segmentation in CT images
  • 2023
  • Ingår i: Computers in Biology and Medicine. - : Elsevier BV. - 0010-4825 .- 1879-0534. ; 158
  • Tidskriftsartikel (refereegranskat)abstract
    • Liver cancer is one of the leading causes of cancer-related deaths worldwide. Automatic liver and tumor segmentation are of great value in clinical practice as they can reduce surgeons' workload and increase the probability of success in surgery. Liver and tumor segmentation is a challenging task because of the different sizes, shapes, blurred boundaries of livers and lesions, and low-intensity contrast between organs within patients. To address the problem of fuzzy livers and small tumors, we propose a novel Residual Multi-scale Attention U-Net (RMAU-Net) for liver and tumor segmentation by introducing two modules, i.e., Res-SE-Block and MAB. The Res-SE-Block can mitigate the problem of gradient disappearance by residual connection and enhance the quality of representations by explicitly modeling the interdependencies and feature recalibration between the channels of features. The MAB can exploit rich multi-scale feature information and capture inter -channel and inter-spatial relationships of features simultaneously. In addition, a hybrid loss function, that combines focal loss and dice loss, is designed to improve segmentation accuracy and speed up convergence. We evaluated the proposed method on two publicly available datasets, i.e., LiTS and 3D-IRCADb. Our proposed method achieved better performance than the other state-of-the-art methods, with dice scores of 0.9552 and 0.9697 for LiTS and 3D-IRCABb liver segmentation, and dice scores of 0.7616 and 0.8307 for LiTS and 3D-IRCABb liver tumor segmentation.
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22.
  • Larsson, Marcus, 1974-, et al. (författare)
  • Assessment of advanced glycated end product accumulation in skin using auto fluorescence multispectral imaging
  • 2017
  • Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 85, s. 106-111
  • Tidskriftsartikel (refereegranskat)abstract
    • Several studies have shown that advanced glycation end products (AGE) play a role in both the microvascular and macrovascular complications of diabetes and are closely linked to inflammation and atherosclerosis. AGEs accumulate in skin and can be detected using their auto fluorescence (AF).A significant correlation exists between AGE AF and the levels of AGEs as obtained from skin biopsies. A commercial device, the AGE Reader, has become available to assess skin AF for clinical purposes but, while displaying promising results, it is limited to single-point measurements performed in contact to skin tissue. Furthermore, in vivo imaging of AGE accumulation is virtually unexplored.We proposed a non-invasive, contact-less novel technique for quantifying fluorescent AGE deposits in skin tissue using a multispectral imaging camera setup (MSI) during ultraviolet (UV) exposure. Imaging involved applying a region-of-interest mask, avoiding specular reflections and a simple calibration. Results of a study conducted on 16 subjects with skin types ranging from fair to deeply pigmented skin, showed that AGE measured with MSI in forearm skin was significantly correlated with the AGE reference method (AGE Reader on forearm skin, R=0.68, p=0.005). AGE measured in facial skin was borderline significantly related to AGE Reader on forearm skin (R=0.47, p=0.078). These results support the use of the technique in devices for non-touch measurement of AGE content in either facial or forearm skin tissue over time.
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24.
  • Lindström, H. Jonathan G., 1992-, et al. (författare)
  • Interplay of mutations, alternate mechanisms, and treatment breaks in leukaemia : Understanding and implications studied with stochastic models
  • 2024
  • Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 169
  • Tidskriftsartikel (refereegranskat)abstract
    • Bcr-Abl1 kinase domain mutations are the most prevalent cause of treatment resistance in chronic myeloid leukaemia (CML). Alternate resistance pathways nevertheless exist, and cell line experiments show certain patterns in the gain, and loss, of some of these alternate adaptations. These adaptations have clinical consequences when the tumour develops mechanisms that are beneficial to its growth under treatment, but slow down its growth when not treated. The results of temporarily halting treatment in CML have not been widely discussed in the clinic and there is no robust theoretical model that could suggest when such a pause in therapy can be tolerated. We constructed a dynamic model of how mechanisms such as Bcr-Abl1 overexpression and drug transporter upregulation evolve to produce resistance in cell lines, and investigate its behaviour subject to different treatment schedules, in particular when the treatment is paused ('drug holiday'). Our study results suggest that the presence of additional resistance mechanisms creates an environment which favours mutations that are either preexisting or occur late during treatment. Importantly, the results suggest the existence of tumour drug addiction, where cancer cells become dependent on the drug for (optimal) survival, which could be exploited through a treatment holiday. All simulation code is available at https://github.com/Sandalmoth/dual-adaptation.
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25.
  • Liu, Qingshan, et al. (författare)
  • Health warning based on 3R ECG Sample's combined features and LSTM
  • 2023
  • Ingår i: Computers in Biology and Medicine. - : Elsevier BV. - 0010-4825 .- 1879-0534. ; 162
  • Tidskriftsartikel (refereegranskat)abstract
    • Most researches use the fixed-length sample to identify ECG abnormalities based on MIT ECG dataset, which leads to information loss. To address this problem, this paper proposes a method for ECG abnormality detection and health warning based on ECG Holter of PHIA and 3R-TSH-L method. The 3R-TSH-L method is implemented by:(1) getting 3R ECG samples using Pan-Tompkins method and using volatility to obtain high-quality raw ECG data; (2) extracting combination features including time-domain features, frequency domain features and time-frequency domain features; (3) using LSTM for classification, training and testing the algorithm based on the MIT-BIH dataset, and obtaining relatively optimal features as spliced normalized fusion features including kurtosis, skewness and RR interval time domain features, STFT-based sub-band spectrum features, and harmonic ratio features. The ECG data were collected using the self-developed ECG Holter (PHIA) on 14 subjects, aged between 24 and 75 including both male and female, to build the ECG dataset (ECG-H). The algorithm was transferred to the ECG-H dataset, and a health warning assessment model based on abnormal ECG rate and heart rate variability weighting was proposed. Experiments show that 3R-TSH-L method proposed in the paper has a high accuracy of 98.28% for the detection of ECG abnormalities of MIT-BIH dataset and a good transfer learning ability of 95.66% accuracy for ECG-H. The health warning model was also testified to be reasonable. The key technique of the ECG Holter of PHIA and the method 3R-TSH-L proposed in this paper is expected to be widely used in family-oriented healthcare.
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26.
  • Längkvist, Martin, 1983-, et al. (författare)
  • Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks
  • 2018
  • Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 97, s. 153-160
  • Tidskriftsartikel (refereegranskat)abstract
    • Computed tomography (CT) is the method of choice for diagnosing ureteral stones - kidney stones that obstruct the ureter. The purpose of this study is to develop a computer aided detection (CAD) algorithm for identifying a ureteral stone in thin slice CT volumes. The challenge in CAD for urinary stones lies in the similarity in shape and intensity of stones with non-stone structures and how to efficiently deal with large high-resolution CT volumes. We address these challenges by using a Convolutional Neural Network (CNN) that works directly on the high resolution CT volumes. The method is evaluated on a large data base of 465 clinically acquired high-resolution CT volumes of the urinary tract with labeling of ureteral stones performed by a radiologist. The best model using 2.5D input data and anatomical information achieved a sensitivity of 100% and an average of 2.68 false-positives per patient on a test set of 88 scans.
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27.
  • Malik, Shairyar, et al. (författare)
  • A novel hybrid meta-heuristic contrast stretching technique for improved skin lesion segmentation
  • 2022
  • Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 151
  • Tidskriftsartikel (refereegranskat)abstract
    • The high precedence of epidemiological examination of skin lesions necessitated the well-performing efficient classification and segmentation models. In the past two decades, various algorithms, especially machine/deep learning-based methods, replicated the classical visual examination to accomplish the above-mentioned tasks. These automated streams of models demand evident lesions with less background and noise affecting the region of interest. However, even after the proposal of these advanced techniques, there are gaps in achieving the efficacy of matter. Recently, many preprocessors proposed to enhance the contrast of lesions, which further aided the skin lesion segmentation and classification tasks. Metaheuristics are the methods used to support the search space optimisation problems. We propose a novel Hybrid Metaheuristic Differential Evolution-Bat Algorithm (DE-BA), which estimates parameters used in the brightness preserving contrast stretching transformation function. For extensive experimentation we tested our proposed algorithm on various publicly available databases like ISIC 2016, 2017, 2018 and PH2, and validated the proposed model with some state-of-the-art already existing segmentation models. The tabular and visual comparison of the results concluded that DE-BA as a preprocessor positively enhances the segmentation results.
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28.
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29.
  • Nelson, Ronald, et al. (författare)
  • Degrees of separation as a statistical tool for evaluating candidate genes
  • 2014
  • Ingår i: Computers in Biology and Medicine. - : Elsevier BV. - 0010-4825 .- 1879-0534. ; 55, s. 49-52
  • Tidskriftsartikel (refereegranskat)abstract
    • Selection of candidate genes is an important step in the exploration of complex genetic architecture. The number of gene networks available is increasing and these can provide information to help with candidate gene selection. It is currently common to use the degree of connectedness in gene networks as validation in Genome Wide Association (GWA) and Quantitative Trait Locus (QTL) mapping studies. However, it can cause misleading results if not validated properly. Here we present a method and tool for validating the gene pairs from GWA studies given the context of the network they co-occur in. It ensures that proposed interactions and gene associations are not statistical artefacts inherent to the specific gene network architecture. The CandidateBacon package provides an easy and efficient method to calculate the average degree of separation (DoS) between pairs of genes to currently available gene networks. We show how these empirical estimates of average connectedness are used to validate candidate gene pairs. Validation of interacting genes by comparing their connectedness with the average connectedness in the gene network will provide support for said interactions by utilising the growing amount of gene network information available. (C) 2014 Elsevier Ltd. All rights reserved.
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30.
  • Nyholm, Joel, et al. (författare)
  • Prediction of dementia based on older adults’ sleep disturbances using machine learning
  • 2024
  • Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 171
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: The most common degenerative condition in older adults is dementia, which can be predicted using a number of indicators and whose progression can be slowed down. One of the indicators of an increased risk of dementia is sleep disturbances. This study aims to examine if machine learning can predict dementia and which sleep disturbance factors impact dementia.Methods: This study uses five machine learning algorithms (gradient boosting, logistic regression, gaussian naive Bayes, random forest and support vector machine) and data on the older population (60+) in Sweden from the Swedish National Study on Ageing and Care — Blekinge (). Each algorithm uses 10-fold stratified cross-validation to obtain the results, which consist of the Brier score for checking accuracy and the feature importance for examining the factors which impact dementia. The algorithms use 16 features which are on personal and sleep disturbance factors.Results: Logistic regression found an association between dementia and sleep disturbances. However, it is slight for the features in the study. Gradient boosting was the most accurate algorithm with 92.9% accuracy, 0.926 f1-score, 0.974 ROC AUC and 0.056 Brier score. The significant factors were different in each machine learning algorithm. If the person sleeps more than two hours during the day, their sex, education level, age, waking up during the night and if the person snores are the variables that most consistently have the highest feature importance in all algorithms.Conclusion: There is an association between sleep disturbances and dementia, which machine learning algorithms can predict. Furthermore, the risk factors for dementia are different across the algorithms, but sleep disturbances can predict dementia.
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31.
  • Ou, Jiajie, et al. (författare)
  • ResTransUnet: An effective network combined with Transformer and U-Net for liver segmentation in CT scans
  • 2024
  • Ingår i: Computers in Biology and Medicine. - : Elsevier BV. - 0010-4825 .- 1879-0534. ; 177
  • Tidskriftsartikel (refereegranskat)abstract
    • Liver segmentation is a fundamental prerequisite for the diagnosis and surgical planning of hepatocellular carcinoma. Traditionally, the liver contour is drawn manually by radiologists using a slice-by-slice method. However, this process is time-consuming and error-prone, depending on the radiologist's experience. In this paper, we propose a new end-to-end automatic liver segmentation framework, named ResTransUNet, which exploits the transformer's ability to capture global context for remote interactions and spatial relationships, as well as the excellent performance of the original U-Net architecture. The main contribution of this paper lies in proposing a novel fusion network that combines Unet and Transformer architectures. In the encoding structure, a dual-path approach is utilized, where features are extracted separately using both convolutional neural networks (CNNs) and Transformer networks. Additionally, an effective feature enhancement unit is designed to transfer the global features extracted by the Transformer network to the CNN for feature enhancement. This model aims to address the drawbacks of traditional Unet-based methods, such as feature loss during encoding and poor capture of global features. Moreover, it avoids the disadvantages of pure Transformer models, which suffer from large parameter sizes and high computational complexity. The experimental results on the LiTS2017 dataset demonstrate remarkable performance for our proposed model, with Dice coefficients, volumetric overlap error (VOE), and relative volume difference (RVD) values for liver segmentation reaching 0.9535, 0.0804, and −0.0007, respectively. Furthermore, to further validate the model's generalization capability, we conducted tests on the 3Dircadb, Chaos, and Sliver07 datasets. The experimental results demonstrate that the proposed method outperforms other closely related models with higher liver segmentation accuracy. In addition, significant improvements can be achieved by applying our method when handling liver segmentation with small and discontinuous liver regions, as well as blurred liver boundaries. The code is available at the website: https://github.com/Jouiry/ResTransUNet.
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32.
  • Panda, Rishab, et al. (författare)
  • Network analysis of chromophore binding site in LOV domain
  • 2023
  • Ingår i: Computers in Biology and Medicine. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0010-4825 .- 1879-0534. ; 161
  • Tidskriftsartikel (refereegranskat)abstract
    • Photoreceptor proteins are versatile toolbox for developing biosensors for optogenetic applications. These molecular tools get activated upon illumination of blue light, which in turn offers a non-invasive method for gaining high spatiotemporal resolution and precise control of cellular signal transduction. The Light-Oxygen-Voltage (LOV) domain family of proteins is a well-recognized system for constructing optogenetic devices. Translation of these proteins into efficient cellular sensors is possible by tuning their photochemistry lifetime. However, the bottleneck is the need for more understanding of the relationship between the protein environment and photocycle kinetics. Significantly, the effect of the local environment also modulates the electronic structure of chromophore, which perturbs the electrostatic and hydrophobic interaction within the binding site. This work highlights the critical factors hidden in the protein networks, linking with their experimental photocycle kinetics. It presents an opportunity to quantitatively examine the alternation in chromophore's equilibrium geometry and identify details which have substantial implications in designing synthetic LOV constructs with desirable photocycle efficiency.
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33.
  • Petrėnas, Andrius, et al. (författare)
  • Low-complexity detection of atrial fibrillation in continuous long-term monitoring.
  • 2015
  • Ingår i: Computers in Biology and Medicine. - : Elsevier BV. - 1879-0534 .- 0010-4825. ; 65:Online 28 January 2015, s. 184-191
  • Tidskriftsartikel (refereegranskat)abstract
    • This study describes an atrial fibrillation (AF) detector whose structure is well-adapted both for detection of subclinical AF episodes and for implementation in a battery-powered device for use in continuous long-term monitoring applications. A key aspect for achieving these two properties is the use of an 8-beat sliding window, which thus is much shorter than the 128-beat window used in most existing AF detectors. The building blocks of the proposed detector include ectopic beat filtering, bigeminal suppression, characterization of RR interval irregularity, and signal fusion. With one design parameter, the performance can be tuned to put more emphasis on avoiding false alarms due to non-AF arrhythmias or more emphasis on detecting brief AF episodes. Despite its very simple structure, the results show that the detector performs better on the MIT-BIH Atrial Fibrillation database than do existing detectors, with high sensitivity and specificity (97.1% and 98.3%, respectively). The detector can be implemented with just a few arithmetical operations and does not require a large memory buffer thanks to the short window.
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34.
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35.
  • Qazi, Sohaib A., et al. (författare)
  • ASIC modelling of SENSE for parallel MRI
  • 2019
  • Ingår i: Computers in Biology and Medicine. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0010-4825 .- 1879-0534. ; 109, s. 53-61
  • Tidskriftsartikel (refereegranskat)abstract
    • Magnetic Resonance Imaging (MRI) is widely used in medical diagnostics and image reconstruction is a vital part of MRI systems. In Parallel MRI (pMRI), imaging process is accelerated by acquiring less data (undersampled) using multiple receiver coils and offline reconstruction algorithms are applied to reconstruct the fully sampled image. In this research, an Application Specific Integrated Circuits (ASIC) model of SENSE (a pMRI algorithm) is presented which reconstructs the image from the undersampled data right on the data acquisition module of the scanner. The proposed ASIC HDL architecture is compared with SENSE reconstruction model implemented on FPGAs, Multi-core CPU and Graphics Processing Units. The proposed architecture is validated using simulated brain data with 8-channel receiver coils and a human cardiac dataset with 20-channel receiver coils. The quality of the reconstructed images is analyzed using Artifact Power (0.0098), Peak Signal-to-Noise Ratio (53.4) and Structured Similarity Index (0.871) which validate the quality of the reconstructed images using the proposed design. The results show that the proposed ASIC HDL SENSE reconstruction model is similar to 8000 times faster as compared to the multi-core CPU reconstruction, similar to 700 times faster than the GPU implementation and similar to 16 times faster as compared to the FPGA reconstruction model. The proposed architecture is suitable for image reconstruction right on the data acquisition system of the scanner and will open new ways for faster image reconstruction on portable MRI scanners.
  •  
36.
  • Qu, Zhiguo, et al. (författare)
  • Quantum conditional generative adversarial network based on patch method for abnormal electrocardiogram generation
  • 2023
  • Ingår i: Computers in Biology and Medicine. - Oxford : Elsevier. - 0010-4825 .- 1879-0534. ; 166, s. 1-13
  • Tidskriftsartikel (refereegranskat)abstract
    • To address the scarcity and class imbalance of abnormal electrocardiogram (ECG) databases, which are crucial in AI-driven diagnostic tools for potential cardiovascular disease detection, this study proposes a novel quantum conditional generative adversarial algorithm (QCGAN-ECG) for generating abnormal ECG signals. The QCGAN-ECG constructs a quantum generator based on patch method. In this method, each sub-generator generates distinct features of abnormal heartbeats in different segments. This patch-based generative algorithm conserves quantum resources and makes QCGAN-ECG practical for near-term quantum devices. Additionally, QCGAN-ECG introduces quantum registers as control conditions. It encodes information about the types and probability distributions of abnormal heartbeats into quantum registers, rendering the entire generative process controllable. Simulation experiments on Pennylane demonstrated that the QCGAN-ECG could generate completely abnormal heartbeats with an average accuracy of 88.8%. Moreover, the QCGAN-ECG can accurately fit the probability distribution of various abnormal ECG data. In the anti-noise experiments, the QCGAN-ECG showcased outstanding robustness across various levels of quantum noise interference. These results demonstrate the effectiveness and potential applicability of the QCGAN-ECG for generating abnormal ECG signals, which will further promote the development of AI-driven cardiac disease diagnosis systems. The source code is available at github.com/VanSWK/QCGAN_ECG. © 2023 The Author(s)
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37.
  • Razvadauskas, Haroldas, et al. (författare)
  • Exploring classical machine learning for identification of pathological lung auscultations
  • 2024
  • Ingår i: Computers in Biology and Medicine. - Oxford : Elsevier. - 0010-4825 .- 1879-0534. ; 168
  • Tidskriftsartikel (refereegranskat)abstract
    • The use of machine learning in biomedical research has surged in recent years thanks to advances in devices and artificial intelligence. Our aim is to expand this body of knowledge by applying machine learning to pulmonary auscultation signals. Despite improvements in digital stethoscopes and attempts to find synergy between them and artificial intelligence, solutions for their use in clinical settings remain scarce. Physicians continue to infer initial diagnoses with less sophisticated means, resulting in low accuracy, leading to suboptimal patient care. To arrive at a correct preliminary diagnosis, the auscultation diagnostics need to be of high accuracy. Due to the large number of auscultations performed, data availability opens up opportunities for more effective sound analysis. In this study, digital 6-channel auscultations of 45 patients were used in various machine learning scenarios, with the aim of distinguishing between normal and abnormal pulmonary sounds. Audio features (such as fundamental frequencies F0-4, loudness, HNR, DFA, as well as descriptive statistics of log energy, RMS and MFCC) were extracted using the Python library Surfboard. Windowing, feature aggregation, and concatenation strategies were used to prepare data for machine learning algorithms in unsupervised (fair-cut forest, outlier forest) and supervised (random forest, regularized logistic regression) settings. The evaluation was carried out using 9-fold stratified cross-validation repeated 30 times. Decision fusion by averaging the outputs for a subject was also tested and found to be helpful. Supervised models showed a consistent advantage over unsupervised ones, with random forest achieving a mean AUC ROC of 0.691 (accuracy 71.11%, Kappa 0.416, F1-score 0.675) in side-based detection and a mean AUC ROC of 0.721 (accuracy 68.89%, Kappa 0.371, F1-score 0.650) in patient-based detection. © Copyright 2024 Elsevier B.V., All rights reserved
  •  
38.
  • Roos, Magnus W, et al. (författare)
  • Computational estimation of fluid mechanical benefits from a fluid deflector at the distal end of artificial vascular grafts
  • 2013
  • Ingår i: Computers in Biology and Medicine. - : Pergamon-Elsevier Science. - 0010-4825 .- 1879-0534. ; 43:2, s. 164-168
  • Tidskriftsartikel (refereegranskat)abstract
    • Intimal hyperplasia at the distal anastomosis is considered to be an important determinant for arterial and arteriovenous graft failure. The connection between unhealthy hemodynamics and intimal hyperplasia motivates the use of computational fluid dynamics modeling to search for improved graft design. However, studies on the fluid mechanical impact on intimal hyperplasia at the suture line intrusion have previously been scanty. In the present work, we focus on intimal hyperplasia at the suture line and illustrate potential benefits from the introduction of a fluid deflector to shield the suture line from unhealthily high wall shear stress.
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39.
  • Roos, Magnus W. (författare)
  • Theoretical estimation of retinal oxygenation during retinal detachment
  • 2007
  • Ingår i: Computers in Biology and Medicine. - : Elsevier BV. - 0010-4825 .- 1879-0534. ; 37:6, s. 890-896
  • Tidskriftsartikel (refereegranskat)abstract
    • The aim of the present work was to simulate the oxygenation of the whole retina during different degrees of retinal detachment. A differential equation describing the oxygenation of the whole retina, at different degrees of detachment, was set up and solved numerically. The results show that the choroid can supply the outer retina with a fairly large amount of oxygen as long as the detachment height is lower than about 1mm. This study thus supports the view that hyperoxia may well prove to be clinically beneficial.  
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40.
  • Sahebi, Golnaz, et al. (författare)
  • GeFeS : A generalized wrapper feature selection approach for optimizing classification performance
  • 2020
  • Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 125
  • Tidskriftsartikel (refereegranskat)abstract
    • In this paper, we propose a generalized wrapper-based feature selection, called GeFeS, which is based on a parallel new intelligent genetic algorithm (GA). The proposed GeFeS works properly under different numerical dataset dimensions and sizes, carefully tries to avoid overfitting and significantly enhances classification accuracy. To make the GA more accurate, robust and intelligent, we have proposed a new operator for features weighting, improved the mutation and crossover operators, and integrated nested cross-validation into the GA process to properly validate the learning model. The k-nearest neighbor (kNN) classifier is utilized to evaluate the goodness of selected features. We have evaluated the efficiency of GeFeS on various datasets selected from the UCI machine learning repository. The performance is compared with state-of-the-art classification and feature selection methods. The results demonstrate that GeFeS can significantly generalize the proposed multi-population intelligent genetic algorithm under different sizes of two-class and multi-class datasets. We have achieved the average classification accuracy of 95.83%, 97.62%, 99.02%, 98.51%, and 94.28% while reducing the number of features from 56 to 28, 34 to 18, 279 to 135, 30 to 16, and 19 to 9 under lung cancer, dermatology, arrhythmia, WDBC, and hepatitis, respectively.
  •  
41.
  • Salehi, Amir M., et al. (författare)
  • Reiterative modeling of combined transcriptomic and proteomic features refines and improves the prediction of early recurrence in squamous cell carcinoma of head and neck
  • 2022
  • Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 149
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Patients with squamous cell carcinoma of the head and neck (SCCHN) have a high-risk of recurrence. We aimed to develop machine learning methods to identify transcriptomic and proteomic features that provide accurate classification models for predicting risk of early recurrence in SCCHN patients.Methods: Clinical, genomic, transcriptomic and proteomic features distinguishing recurrence risk were examined in SCCHN patients from The Cancer Genome Atlas (TCGA). Recurrence within one year after treatment was classified as high-risk and no recurrence as low-risk.Results: No significant differences in individual clinicopathological characteristics, mutation profiles or mRNA expression patterns were seen between the groups using conventional statistical analysis. Using the machine learning algorithm, extreme gradient boosting (XGBoost), ten proteins (RAD50, 4E-BP1, MYH11, MAP2K1, BECN1, NF2, RAB25, ERRFI1, KDR, SERPINE1) and five mRNAs (PLAUR, DKK1, AXIN2, ANG and VEGFA) made the greatest contribution to classification. These features were used to build improved models in XGBoost, achieving the best discrimination performance when combining transcriptomic and proteomic data, providing an accuracy of 0.939 and an Area Under the ROC Curve (AUC) of 0.951.Conclusions: This study highlights machine learning to identify transcriptomic and proteomic factors that play important roles in predicting risk of recurrence in patients with SCCHN and to develop such models by iterative cycles to enhance their accuracy, thereby aiding the introduction of personalized treatment regimens.
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42.
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43.
  • Schilcher, Jörg, 1978-, et al. (författare)
  • Fusion of electronic health records and radiographic images for a multimodal deep learning prediction model of atypical femur fractures
  • 2024
  • Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 168
  • Tidskriftsartikel (refereegranskat)abstract
    • Atypical femur fractures (AFF) represent a very rare type of fracture that can be difficult to discriminate radiologically from normal femur fractures (NFF). AFFs are associated with drugs that are administered to prevent osteoporosis-related fragility fractures, which are highly prevalent in the elderly population. Given that these fractures are rare and the radiologic changes are subtle currently only 7% of AFFs are correctly identified, which hinders adequate treatment for most patients with AFF. Deep learning models could be trained to classify automatically a fracture as AFF or NFF, thereby assisting radiologists in detecting these rare fractures. Historically, for this classification task, only imaging data have been used, using convolutional neural networks (CNN) or vision transformers applied to radiographs. However, to mimic situations in which all available data are used to arrive at a diagnosis, we adopted an approach of deep learning that is based on the integration of image data and tabular data (from electronic health records) for 159 patients with AFF and 914 patients with NFF. We hypothesized that the combinatorial data, compiled from all the radiology departments of 72 hospitals in Sweden and the Swedish National Patient Register, would improve classification accuracy, as compared to using only one modality. At the patient level, the area under the ROC curve (AUC) increased from 0.966 to 0.987 when using the integrated set of imaging data and seven pre-selected variables, as compared to only using imaging data. More importantly, the sensitivity increased from 0.796 to 0.903. We found a greater impact of data fusion when only a randomly selected subset of available images was used to make the image and tabular data more balanced for each patient. The AUC then increased from 0.949 to 0.984, and the sensitivity increased from 0.727 to 0.849.These AUC improvements are not large, mainly because of the already excellent performance of the CNN (AUC of 0.966) when only images are used. However, the improvement is clinically highly relevant considering the importance of accuracy in medical diagnostics. We expect an even greater effect when imaging data from a clinical workflow, comprising a more diverse set of diagnostic images, are used.
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44.
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45.
  • Shamohammadi, Hossein, et al. (författare)
  • 3D numerical simulation of hot airflow in the human nasal cavity and trachea
  • 2022
  • Ingår i: Computers in Biology and Medicine. - : Elsevier BV. - 0010-4825 .- 1879-0534. ; 147, s. 105702-
  • Tidskriftsartikel (refereegranskat)abstract
    • Background and objective: The primary function of the human respiratory system is gas and moisture exchange, and conditioning inhaled air to prevent damage to the lungs and alveoli. In a fire incident, exposed soft tissues contract and the respiratory system may be severely damaged, possibly leading to respiratory failure and even respiratory arrest. The purpose of this study is to numerically simulate hot airflow in the human upper airway and trachea to investigate heat and moisture transfer and induced thermal injuries. Methods: For analysis, the airflow is assumed to be laminar and steady, and simulations have been carried out at volume flow rates of 5 and 10 L/min, inlet temperatures of 70-240 ?C, and relative humidity up to 40%. The mucous layer and surrounding tissues are incorporated into the conducting zone of the model. The blood perfusion is considered at different rates up to 5(Kg/m3.s) to regulate the temperature, and the vapor concentration is coupled with the energy equation. Results: The temperature and humidity distribution on the airway wall were calculated for all the studied conditions in order to find the mild and severe burn for different inhaled air temperatures. At the inlet temperatures of 70 and 100 ?C, there are mild burns in several nasal cavity regions. At the higher temperatures of 160 and 200 ?C, these areas suffer from severe burns and mild burns occur at the superior parts and nasopharynx. Rapid evaporation and tissue destruction will be observed if anyone breathes the 240 ?C air. Conclusions: The results show that the hot inlet temperatures drop below 44 ?C when passing through the upper airway, and the lower airway was not affected. Increasing the inlet temperature from 70 to 240 ?C extends the burns from mild to severe and the affected areas from the beginning of the nasal cavity to the pharynx.
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46.
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47.
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48.
  • Singh, Ram, et al. (författare)
  • Impact of quarantine on fractional order dynamical model of Covid-19
  • 2022
  • Ingår i: Computers in Biology and Medicine. - Oxford : Elsevier. - 0010-4825 .- 1879-0534. ; 151, Part A
  • Tidskriftsartikel (refereegranskat)abstract
    • In this paper, a Covid-19 dynamical transmission model of a coupled non-linear fractional differential equation in the Atangana-Baleanu Caputo sense is proposed. The basic dynamical transmission features of the proposed system are briefly discussed. The qualitative as well as quantitative results on the existence and uniqueness of the solutions are evaluated through the fixed point theorem. The Ulam-Hyers stability analysis of the suggested system is established. The two-step Adams-Bashforth-Moulton (ABM) numerical method is employed to find its numerical solution. The numerical simulation is performed to accesses the impact of various biological parameters on the dynamics of Covid-19 disease. © 2022 The Author(s). Published by Elsevier Ltd. All rights reserved.
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49.
  • Stasiunas, Antanas, et al. (författare)
  • A multi-channel adaptive nonlinear filtering structure realizingsome properties of the hearing system
  • 2005
  • Ingår i: Computers in Biology and Medicine. - Amsterdam : Elsevier. - 0010-4825 .- 1879-0534. ; 35:6, s. 495-510
  • Tidskriftsartikel (refereegranskat)abstract
    • An adaptive nonlinear signal-filtering model of the cochlea is proposed based on the functional properties of the inner ear. The model consists of the cochlear filtering segments taking into account the longitudinal, transverse and radial pressure wave propagation. On the basis of an analytical description of different parts of the model and the results of computer modeling, the biological significance of the nonlinearity of signal transduction processes in the outer hair cells, their role in signal compression and adaptation, the efferent control over the characteristics of the filtering structures (frequency selectivity and sensitivity) are explained. © 2004 Elsevier Ltd. All rights reserved.
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50.
  • Stasiunas, Antanas, et al. (författare)
  • An adaptive model simulating the somatic motility and the active hair bundle motion of the OHC
  • 2009
  • Ingår i: Computers in Biology and Medicine. - New York : Pergamon. - 0010-4825 .- 1879-0534. ; 39:9, s. 800-809
  • Tidskriftsartikel (refereegranskat)abstract
    • The outer hair cells (OHC) of the mammalian inner ear change the sensitivity and frequency selectivity of the filtering system of the cochlea using two kinds of mechanical activity: the somatic motility and the active hair bundle motion. We designed a non-linear adaptive model of the OHC employing both mechanisms of the mechanical activity. The modeling results show that the high sensitivity and frequency selectivity of the filtering system of the cochlea depend on the somatic motility of the OHC. However, both mechanisms of mechanical activity are involved in the adaptation to sound intensity and efferent-synaptic influence. The fast (alternating) component (AC) of the mechanical–electrical transduction signal controls the motor protein prestin and fast changes in axial length of the cell. The slow (direct) component (DC) appearing at high signal intensity affects the axial stiffness, the cell length and the position of the hair bundle. The efferent influence is realized by the same mechanism.
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