| 1. |
- Ge, Chenjie, 1991, et al.
(författare)
-
Enlarged Training Dataset by Pairwise GANs for Molecular-Based Brain Tumor Classification
- 2020
-
Ingår i: IEEE Access. - 2169-3536 .- 2169-3536. ; 8:1, s. 22560-22570
-
Tidskriftsartikel (refereegranskat)abstract
- This paper addresses issues of brain tumor subtype classification using Magnetic Resonance Images (MRIs) from different scanner modalities like T1 weighted, T1 weighted with contrast-enhanced, T2 weighted and FLAIR images. Currently most available glioma datasets are relatively moderate in size, and often accompanied with incomplete MRIs in different modalities. To tackle the commonly encountered problems of insufficiently large brain tumor datasets and incomplete modality of image for deep learning, we propose to add augmented brain MR images to enlarge the training dataset by employing a pairwise Generative Adversarial Network (GAN) model. The pairwise GAN is able to generate synthetic MRIs across different modalities. To achieve the patient-level diagnostic result, we propose a post-processing strategy to combine the slice-level glioma subtype classification results by majority voting. A two-stage course-to-fine training strategy is proposed to learn the glioma feature using GAN-augmented MRIs followed by real MRIs. To evaluate the effectiveness of the proposed scheme, experiments have been conducted on a brain tumor dataset for classifying glioma molecular subtypes: isocitrate dehydrogenase 1 (IDH1) mutation and IDH1 wild-type. Our results on the dataset have shown good performance (with test accuracy 88.82%). Comparisons with several state-of-the-art methods are also included.
|
|
| 2. |
- Ali, Muhaddisa Barat, 1986, et al.
(författare)
-
A novel federated deep learning scheme for glioma and its subtype classification
- 2023
-
Ingår i: Frontiers in Neuroscience. - 1662-4548 .- 1662-453X. ; 17
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Deep learning (DL) has shown promising results in molecular-based classification of glioma subtypes from MR images. DL requires a large number of training data for achieving good generalization performance. Since brain tumor datasets are usually small in size, combination of such datasets from different hospitals are needed. Data privacy issue from hospitals often poses a constraint on such a practice. Federated learning (FL) has gained much attention lately as it trains a central DL model without requiring data sharing from different hospitals. Method: We propose a novel 3D FL scheme for glioma and its molecular subtype classification. In the scheme, a slice-based DL classifier, EtFedDyn, is exploited which is an extension of FedDyn, with the key differences on using focal loss cost function to tackle severe class imbalances in the datasets, and on multi-stream network to exploit MRIs in different modalities. By combining EtFedDyn with domain mapping as the pre-processing and 3D scan-based post-processing, the proposed scheme makes 3D brain scan-based classification on datasets from different dataset owners. To examine whether the FL scheme could replace the central learning (CL) one, we then compare the classification performance between the proposed FL and the corresponding CL schemes. Furthermore, detailed empirical-based analysis were also conducted to exam the effect of using domain mapping, 3D scan-based post-processing, different cost functions and different FL schemes. Results: Experiments were done on two case studies: classification of glioma subtypes (IDH mutation and wild-type on TCGA and US datasets in case A) and glioma grades (high/low grade glioma HGG and LGG on MICCAI dataset in case B). The proposed FL scheme has obtained good performance on the test sets (85.46%, 75.56%) for IDH subtypes and (89.28%, 90.72%) for glioma LGG/HGG all averaged on five runs. Comparing with the corresponding CL scheme, the drop in test accuracy from the proposed FL scheme is small (−1.17%, −0.83%), indicating its good potential to replace the CL scheme. Furthermore, the empirically tests have shown that an increased classification test accuracy by applying: domain mapping (0.4%, 1.85%) in case A; focal loss function (1.66%, 3.25%) in case A and (1.19%, 1.85%) in case B; 3D post-processing (2.11%, 2.23%) in case A and (1.81%, 2.39%) in case B and EtFedDyn over FedAvg classifier (1.05%, 1.55%) in case A and (1.23%, 1.81%) in case B with fast convergence, which all contributed to the improvement of overall performance in the proposed FL scheme. Conclusion: The proposed FL scheme is shown to be effective in predicting glioma and its subtypes by using MR images from test sets, with great potential of replacing the conventional CL approaches for training deep networks. This could help hospitals to maintain their data privacy, while using a federated trained classifier with nearly similar performance as that from a centrally trained one. Further detailed experiments have shown that different parts in the proposed 3D FL scheme, such as domain mapping (make datasets more uniform) and post-processing (scan-based classification), are essential.
|
|
| 3. |
- Ali, Muhaddisa Barat, 1986, et al.
(författare)
-
Multi-stream Convolutional Autoencoder and 2D Generative Adversarial Network for Glioma Classification
- 2019
-
Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. ; 11678 LNCS, s. 234-245
-
Konferensbidrag (refereegranskat)abstract
- Diagnosis and timely treatment play an important role in preventing brain tumor growth. Deep learning methods have gained much attention lately. Obtaining a large amount of annotated medical data remains a challenging issue. Furthermore, high dimensional features of brain images could lead to over-fitting. In this paper, we address the above issues. Firstly, we propose an architecture for Generative Adversarial Networks to generate good quality synthetic 2D MRIs from multi-modality MRIs (T1 contrast-enhanced, T2, FLAIR). Secondly, we propose a deep learning scheme based on 3-streams of Convolutional Autoencoders (CAEs) followed by sensor information fusion. The rational behind using CAEs is that it may improve glioma classification performance (as comparing with conventional CNNs), since CAEs offer noise robustness and also efficient feature reduction hence possibly reduce the over-fitting. A two-round training strategy is also applied by pre-training on GAN augmented synthetic MRIs followed by refined-training on original MRIs. Experiments on BraTS 2017 dataset have demonstrated the effectiveness of the proposed scheme (test accuracy 92.04%). Comparison with several exiting schemes has provided further support to the proposed scheme.
|
|
| 4. |
- Henje, Catharina, 1960-, et al.
(författare)
-
Obstacles and risks in the traffic environment for users of powered wheelchairs in Sweden
- 2021
-
Ingår i: Accident Analysis and Prevention. - : Elsevier. - 0001-4575 .- 1879-2057. ; 159
-
Tidskriftsartikel (refereegranskat)abstract
- Objective: According to the European Union, fatal road accidents involving Vulnerable Road Users (VRUs) are equal in proportion to fatal car road accidents (46%). VRUs include individuals with mobility challenges such as the elderly and Powered Wheelchair (PWC) users. The aim of this interdisciplinary qualitative study was to identify obstacles and risks for PWC users by exploring their behaviour and experiences in traffic environments.Methods: Videos and in-depth interviews with 13 PWC users aged 20–66 were analysed for this study. The interviews and videos, which include real-life outdoor observations, originate from a qualitative study exploring experiences of PWC use on a daily basis in Sweden. Underlying causal factors to identified risks and obstacles were identified, based on human, vehicle (PWC) and environmental factors in accordance with the Haddon Matrix.Results: The results show significant potential for improvement within all three perspectives of the Haddon Matrix used in the analysis. Participants faced and dealt with various obstacles and risks in order to reach their destination. For example, this includes uneven surfaces, differences in ground levels, steep slopes, as well as interactions with other road users and the influence of weather conditions, resulting in PWC users constantly accommodating and coping with the shortcomings of the vehicle and the environment.Conclusions: There are still major challenges with regard to preventing obstacles and risks in the traffic environment for PWC users. To discern PWC users in traffic accident and injury data bases, a start would be to register type of aid used for persons involved in an accident. Furthermore, to emphasise PWC users’ role as VRUs, it may also be advantageous to describe them as drivers rather than users when navigating the traffic environment. Given the limited sample, further research covering more data from a broader perspective would be beneficial. By incorporating emerging knowledge of PWC users’ prerequisites and needs, and including them in research and traffic planning, the society will grow safer and more inclusive, and become better prepared for meeting future demands on accessibility from an aging population.
|
|
| 5. |
- Ge, Chenjie, 1991, et al.
(författare)
-
Cross-Modality Augmentation of Brain Mr Images Using a Novel Pairwise Generative Adversarial Network for Enhanced Glioma Classification
- 2019
-
Ingår i: Proceedings - International Conference on Image Processing, ICIP. - 1522-4880.
-
Konferensbidrag (refereegranskat)abstract
- © 2019 IEEE. Brain Magnetic Resonance Images (MRIs) are commonly used for tumor diagnosis. Machine learning for brain tumor characterization often uses MRIs from many modalities (e.g., T1-MRI, Enhanced-T1-MRI, T2-MRI and FLAIR). This paper tackles two issues that may impact brain tumor characterization performance from deep learning: insufficiently large training dataset, and incomplete collection of MRIs from different modalities. We propose a novel pairwise generative adversarial network (GAN) architecture for generating synthetic brain MRIs in missing modalities by using existing MRIs in other modalities. By improving the training dataset, we aim to mitigate the overfitting and improve the deep learning performance. Main contributions of the paper include: (a) propose a pairwise generative adversarial network (GAN) for brain image augmentation via cross-modality image generation; (b) propose a training strategy to enhance the glioma classification performance, where GAN-augmented images are used for pre-training, followed by refined-training using real brain MRIs; (c) demonstrate the proposed method through tests and comparisons of glioma classifiers that are trained from mixing real and GAN synthetic data, as well as from real data only. Experiments were conducted on an open TCGA dataset, containing 167 subjects for classifying IDH genotypes (mutation or wild-type). Test results from two experimental settings have both provided supports to the proposed method, where glioma classification performance has consistently improved by using mixed real and augmented data (test accuracy 81.03%, with 2.57% improvement).
|
|
| 6. |
- Altay, Özlem, et al.
(författare)
-
Combined Metabolic Activators Accelerates Recovery in Mild-to-Moderate COVID-19
- 2021
-
Ingår i: Advanced Science. - : Wiley. - 2198-3844. ; 8:17
-
Tidskriftsartikel (refereegranskat)abstract
- COVID-19 is associated with mitochondrial dysfunction and metabolic abnormalities, including the deficiencies in nicotinamide adenine dinucleotide (NAD+) and glutathione metabolism. Here it is investigated if administration of a mixture of combined metabolic activators (CMAs) consisting of glutathione and NAD+ precursors can restore metabolic function and thus aid the recovery of COVID-19 patients. CMAs include l-serine, N-acetyl-l-cysteine, nicotinamide riboside, and l-carnitine tartrate, salt form of l-carnitine. Placebo-controlled, open-label phase 2 study and double-blinded phase 3 clinical trials are conducted to investigate the time of symptom-free recovery on ambulatory patients using CMAs. The results of both studies show that the time to complete recovery is significantly shorter in the CMA group (6.6 vs 9.3 d) in phase 2 and (5.7 vs 9.2 d) in phase 3 trials compared to placebo group. A comprehensive analysis of the plasma metabolome and proteome reveals major metabolic changes. Plasma levels of proteins and metabolites associated with inflammation and antioxidant metabolism are significantly improved in patients treated with CMAs as compared to placebo. The results show that treating patients infected with COVID-19 with CMAs lead to a more rapid symptom-free recovery, suggesting a role for such a therapeutic regime in the treatment of infections leading to respiratory problems.
|
|
| 7. |
- Lappa, Dimitra, 1988, et al.
(författare)
-
Self-organized metabotyping of obese individuals identifies clusters responding differently to bariatric surgery
- 2023
-
Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203 .- 1932-6203. ; 18:3, s. e0279335-
-
Tidskriftsartikel (refereegranskat)abstract
- Weight loss through bariatric surgery is efficient for treatment or prevention of obesity related diseases such as type 2 diabetes and cardiovascular disease. Long term weight loss response does, however, vary among patients undergoing surgery. Thus, it is difficult to identify predictive markers while most obese individuals have one or more comorbidities. To overcome such challenges, an in-depth multiple omics analyses including fasting peripheral plasma metabolome, fecal metagenome as well as liver, jejunum, and adipose tissue transcriptome were performed for 106 individuals undergoing bariatric surgery. Machine leaning was applied to explore the metabolic differences in individuals and evaluate if metabolism-based patients' stratification is related to their weight loss responses to bariatric surgery. Using Self-Organizing Maps (SOMs) to analyze the plasma metabolome, we identified five distinct metabotypes, which were differentially enriched for KEGG pathways related to immune functions, fatty acid metabolism, protein-signaling, and obesity pathogenesis. The gut metagenome of the most heavily medicated metabotypes, treated simultaneously for multiple cardiometabolic comorbidities, was significantly enriched in Prevotella and Lactobacillus species. This unbiased stratification into SOM-defined metabotypes identified signatures for each metabolic phenotype and we found that the different metabotypes respond differently to bariatric surgery in terms of weight loss after 12 months. An integrative framework that utilizes SOMs and omics integration was developed for stratifying a heterogeneous bariatric surgery cohort. The multiple omics datasets described in this study reveal that the metabotypes are characterized by a concrete metabolic status and different responses in weight loss and adipose tissue reduction over time. Our study thus opens a path to enable patient stratification and hereby allow for improved clinical treatments.
|
|
| 8. |
- Ge, Chenjie, 1991, et al.
(författare)
-
Deep semi-supervised learning for brain tumor classification
- 2020
-
Ingår i: BMC Medical Imaging. - : Springer Science and Business Media LLC. - 1471-2342. ; 20:1
-
Tidskriftsartikel (refereegranskat)abstract
- Background: This paper addresses issues of brain tumor, glioma, classification from four modalities of Magnetic Resonance Image (MRI) scans (i.e., T1 weighted MRI, T1 weighted MRI with contrast-enhanced, T2 weighted MRI and FLAIR). Currently, many available glioma datasets often contain some unlabeled brain scans, and many datasets are moderate in size. Methods: We propose to exploit deep semi-supervised learning to make full use of the unlabeled data. Deep CNN features were incorporated into a new graph-based semi-supervised learning framework for learning the labels of the unlabeled data, where a new 3D-2D consistent constraint is added to make consistent classifications for the 2D slices from the same 3D brain scan. A deep-learning classifier is then trained to classify different glioma types using both labeled and unlabeled data with estimated labels. To alleviate the overfitting caused by moderate-size datasets, synthetic MRIs generated by Generative Adversarial Networks (GANs) are added in the training of CNNs. Results: The proposed scheme has been tested on two glioma datasets, TCGA dataset for IDH-mutation prediction (molecular-based glioma subtype classification) and MICCAI dataset for glioma grading. Our results have shown good performance (with test accuracies 86.53% on TCGA dataset and 90.70% on MICCAI dataset). Conclusions: The proposed scheme is effective for glioma IDH-mutation prediction and glioma grading, and its performance is comparable to the state-of-the-art.
|
|
| 9. |
- Shannon, Michelle M., et al.
(författare)
-
Can the physical environment itself influence neurological patient activity?
- 2019
-
Ingår i: Disability and Rehabilitation. - : Informa UK Limited. - 1464-5165 .- 0963-8288. ; 41:10, s. 1177-1189
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose: To evaluate if a changed physical environment following redesign of a hospital ward influenced neurological patient physical and social activity. Methods: A “before and after” observational design was used that included 17 acute neurological patients pre-move (median age 77 (IQR 69–85) years Ward A and 20 post-move (median age 70 (IQR 57–81) years Ward B. Observations occurred for 1 day from 08.00–17.00 using Behavioral Mapping of patient physical and social activity, and location of that activity. Staff and ward policies remained unchanged throughout. An Environmental Description Checklist of each ward was also completed. Results: Behavioral Mapping was conducted pre-/post-move with a total of 801 Ward A and 918 Ward B observations. Environmental Description Checklists showed similarities in design features in both neurological wards with similar numbers of de-centralized nursing stations, however there were more single rooms and varied locations to congregate in Ward B (30% more single-patient rooms and separate allied health therapy room). Patients were alone >60% of time in both wards, although there was more in bed social activity in Ward A and more out of bed social activity in Ward B. There were low amounts of physical activity outside of patient rooms in both wards. Significantly more physical activity occurred in Ward B patient rooms (median = 47%, IQR 14–74%) compared to Ward A (median = 2% IQR 0–14%), Wilcoxon Rank Sum test z = −3.28, p = 0.001. Conclusions: Overall, patient social and physical activity was low, with little to no use of communal spaces. However we found more physical activity in patient rooms in the Ward B environment. Given the potential for patient activity to drive brain reorganization and repair, the physical environment should be considered an active factor in neurological rehabilitation and recovery.Implications for Rehabilitation Clinicians should include consideration of the impact of physical environment on physical and social activity of neurological patients when designing therapeutic rehabilitation environments. Despite architectural design intentions patient and social activity opportunities can be limited. Optimal neurological patient neuroplasticity and recovery requires sufficient environmental challenge, however current hospital environments for rehabilitation do not provide this.
|
|
| 10. |
- Inci, Kamuran, et al.
(författare)
-
Air bubbles are released by thoracic endograft deployment: An in vitro experimental study
- 2016
-
Ingår i: SAGE Open Medicine. - : SAGE Publications. - 2050-3121. ; 4
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose: Embolic stroke is a dreaded complication of thoracic endovascular aortic repair. The prevailing theory about its cause is that particulate debris from atherosclerotic lesions in the aortic wall are dislodged by endovascular instruments and embolize to the brain. An alternative source of embolism might be air trapped in the endograft delivery system. The aim of this experimental study was to determine whether air is released during deployment of a thoracic endograft. Methods: In an experimental benchtop study, eight thoracic endografts (five Medtronic Valiant Thoracic and three Gore TAG) were deployed in a water-filled transparent container drained from air. Endografts were prepared and deployed according to their instructions for use. Deployment was filmed and the volume of air released was collected and measured in a calibrated syringe. Results: Air was released from all the endografts examined. Air volumes ranged from 0.1 to 0.3 mL for Medtronic Valiant Thoracic and from <0.025 to 0.04 mL for Gore TAG. The largest bubbles had a diameter of approximately 3 mm and came from the proximal end of the Medtronic Valiant device. Conclusion: Air bubbles are released from thoracic endografts during deployment. Air embolism may be an alternative cause of stroke during thoracic endovascular aortic repair.
|
|
