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1.
  • 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.
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2.
  • 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.
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3.
  • Gryska, Emilia, 1992, et al. (författare)
  • Deep learning for automatic brain tumour segmentation on MRI: evaluation of recommended reporting criteria via a reproduction and replication study.
  • 2022
  • Ingår i: BMJ open. - : BMJ. - 2044-6055. ; 12:7
  • Tidskriftsartikel (refereegranskat)abstract
    • To determine the reproducibility and replicability of studies that develop and validate segmentation methods for brain tumours on MRI and that follow established reproducibility criteria; and to evaluate whether the reporting guidelines are sufficient.Two eligible validation studies of distinct deep learning (DL) methods were identified. We implemented the methods using published information and retraced the reported validation steps. We evaluated to what extent the description of the methods enabled reproduction of the results. We further attempted to replicate reported findings on a clinical set of images acquired at our institute consisting of high-grade and low-grade glioma (HGG, LGG), and meningioma (MNG) cases.We successfully reproduced one of the two tumour segmentation methods. Insufficient description of the preprocessing pipeline and our inability to replicate the pipeline resulted in failure to reproduce the second method. The replication of the first method showed promising results in terms of Dice similarity coefficient (DSC) and sensitivity (Sen) on HGG cases (DSC=0.77, Sen=0.88) and LGG cases (DSC=0.73, Sen=0.83), however, poorer performance was observed for MNG cases (DSC=0.61, Sen=0.71). Preprocessing errors were identified that contributed to low quantitative scores in some cases.Established reproducibility criteria do not sufficiently emphasise description of the preprocessing pipeline. Discrepancies in preprocessing as a result of insufficient reporting are likely to influence segmentation outcomes and hinder clinical utilisation. A detailed description of the whole processing chain, including preprocessing, is thus necessary to obtain stronger evidence of the generalisability of DL-based brain tumour segmentation methods and to facilitate translation of the methods into clinical practice.
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4.
  • Bayadsi, Haytham, 1987-, et al. (författare)
  • The correlation between small papillary thyroid cancers and gamma radionuclides Cs-137, Th-232, U-238 and K-40 using spatially-explicit, register-based methods
  • 2023
  • Ingår i: Spatial and Spatio-Temporal Epidemiology. - : Elsevier. - 1877-5845 .- 1877-5853. ; 47
  • Tidskriftsartikel (refereegranskat)abstract
    • A steep increase of small papillary thyroid cancers (sPTCs) has been observed globally. A major risk factor for developing PTC is ionizing radiation. The aim of this study is to investigate the spatial distribution of sPTC in Sweden and the extent to which prevalence is correlated to gamma radiation levels (Caesium-137 (Cs-137), Thorium-232 (Th-232), Uranium-238 (U-238) and Potassium-40 (K-40)) using multiple geospatial and geo-statistical methods. The prevalence of metastatic sPTC was associated with significantly higher levels of Gamma radiation from Th-232, U-238 and K-40. The association is, however, inconsistent and the prevalence is higher in densely populated areas. The results clearly indicate that sPTC has causative factors that are neither evenly distributed among the population, nor geographically, calling for further studies with bigger cohorts. Environ-mental factors are believed to play a major role in the pathogenesis of the disease.
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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).
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6.
  • Sadeghi, B., et al. (författare)
  • Early-phase GVHD gene expression profile in target versus non-target tissues : kidney, a possible target?
  • 2013
  • Ingår i: Bone Marrow Transplantation. - : Springer Science and Business Media LLC. - 0268-3369 .- 1476-5365. ; 48:2, s. 284-293
  • Tidskriftsartikel (refereegranskat)abstract
    • GVHD is a major complication after allo-SCT. In GVHD, some tissues like liver, intestine and skin are infiltrated by donor T cells while others like muscle are not. The mechanism underlying targeted tropism of donor T cells is not fully understood. In the present study, we aim to explore differences in gene expression profile among target versus non-target tissues in a mouse model of GVHD based on chemotherapy conditioning. Expression levels of JAK-signal transducers and activators of transcription (STAT), CXCL1, ICAM1 and STAT3 were increased in the liver and remained unchanged (or decreased) in the muscle and kidney after conditioning. At the start of GVHD the expression levels of CXCL9, ITGb2, SAA3, MARCO, TLR and VCAM1 were significantly higher in the liver or kidney compared with the muscle of GVHD animals. Moreover, biological processes of inflammatory reactions, leukocyte migration, response to bacterium and chemotaxis followed the same pattern. Our data show that both chemotherapy and allogenicity exclusively induce expression of inflammatory genes in target tissues. Moreover, gene expression profile and histopathological findings in the kidney are similar to those observed in the liver of GVHD mice. Bone Marrow Transplantation (2013) 48, 284-293; doi:10.1038/bmt.2012.120; published online 23 July 2012
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7.
  • Hammarström, Helena, et al. (författare)
  • Fungal Tracheobronchitis in Lung Transplant Recipients : Incidence and Utility of Diagnostic Markers
  • 2023
  • Ingår i: Journal of Fungi. - : MDPI AG. - 2309-608X. ; 9:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Fungal tracheobronchitis caused by Aspergillus and Candida spp. is a recognized complication after lung transplantation, but knowledge of the incidence of Candida tracheobronchitis is lacking. The diagnosis relies on fungal cultures in bronchoalveolar lavage fluid (BALF), but cultures have low specificity. We aimed to evaluate the one-year incidence of fungal tracheobronchitis after lung transplantation and to assess the utility of diagnostic markers in serum and BALF to discriminate fungal tracheobronchitis from colonization. Ninety-seven consecutively included adult lung-transplant recipients were prospectively followed. BALF and serum samples were collected at 1, 3 and 12 months after transplantation and analyzed for betaglucan (serum and BALF), neutrophils (BALF) and galactomannan (BALF). Fungal tracheobronchitis was defined according to consensus criteria, modified to include Candida as a mycologic criterion. The cumulative one-year incidence of Candida and Aspergillus tracheobronchitis was 23% and 16%, respectively. Neutrophils of >75% of total leukocytes in BALF had 92% specificity for Candida tracheobronchitis. The area under the ROC curves for betaglucan and galactomannan in BALF to discriminate Aspergillus tracheobronchitis from colonization or no fungal infection were high (0.86 (p < 0.0001) and 0.93 (p < 0.0001), respectively). To conclude, the one-year incidence of fungal tracheobronchitis after lung transplantation was high and dominated by Candida spp. Diagnostic markers in BALF could be useful to discriminate fungal colonization from tracheobronchitis.
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8.
  • 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.
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9.
  • Eldh, Maria, 1980, et al. (författare)
  • MicroRNA in exosomes isolated directly from the liver circulation in patients with metastatic uveal melanoma
  • 2014
  • Ingår i: BMC Cancer. - London : Springer Science and Business Media LLC. - 1471-2407. ; 14
  • Tidskriftsartikel (refereegranskat)abstract
    • Uveal melanoma is a tumour arising from melanocytes of the eye, and 30 per cent of these patients develop liver metastases. Exosomes are small RNA containing nano-vesicles released by most cells, including malignant melanoma cells. This clinical translational study included patients undergoing isolated hepatic perfusion (IHP) for metastatic uveal melanoma, from whom exosomes were isolated directly from liver perfusates. The objective was to determine whether exosomes are present in the liver circulation, and to ascertain whether these may originate from melanoma cells.
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10.
  • 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.
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