| 1. |
- Ge, Chenjie, 1991, et al.
(författare)
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Enlarged Training Dataset by Pairwise GANs for Molecular-Based Brain Tumor Classification
- 2020
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Ingår i: IEEE Access. - 2169-3536 .- 2169-3536. ; 8:1, s. 22560-22570
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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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| 2. |
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| 3. |
- Ali, Muhaddisa Barat, 1986, et al.
(författare)
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A novel federated deep learning scheme for glioma and its subtype classification
- 2023
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Ingår i: Frontiers in Neuroscience. - 1662-4548 .- 1662-453X. ; 17
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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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| 4. |
- Petersson, Jesper, 1974
(författare)
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Medicine At A Distance In Sweden: Spatiotemporal Matters In Accomplishing Working Telemedicine
- 2011
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Ingår i: Science Studies. - 0786-3012. ; 24:2, s. 43-62
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Tidskriftsartikel (refereegranskat)abstract
- This paper examines the accomplishment of making technology work, using the discourse around telemedicine in Swedish healthcare during 1994-2003. The paper will compare four projects launched in the mid-1990s and policymakers’ visions of healthcare through telemedicine. I will employ a sociotechnical approach developed within Actor-Network Theory that understands functioning technology not as something intrinsic but as an outcome of an ongoing process of negotiations. In the paper, I will extend the sociotechnical approach of what constitutes working technology to include spatiotemporal matters. I will also approach the closely related issue of space that has become a concern of Actor-Network Theory scholars interested in the accomplishment and continued workings of technology as it travels. In this discussion, an emphasis on fixed relations (network space) has been challenged by investigations into changing relations (fluid space). This paper suggests that in order to travel well, technology must be both fixed and fluid.⁰
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| 6. |
- Björkman, Kristoffer, et al.
(författare)
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Genotype-phenotype correlations in patients with complex I deficiency due to mutations in NDUFS1 and NDUFV1
- 2014
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Ingår i: Euromit 2014, 15-19 juni, Tampere, Finland.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Objectives: To study genotype-phenotype correlations in genes encoding complex I electron input module subunits. Materials and methods: We studied five patients with isolated complex I deficiency, three with NDUFS1 mutations and two with NDUFV1 mutations. A literature review of all reported cases of mutations in the affected genes was performed. Results: The literature review revealed pathological mutations in NDUFS1 for 18 patients in 17 families and correspondingly in NDUFV1 for 26 patients in 19 families. Unpublished clinical data for our five patients were added. Our study showed quite variable clinical courses; death before two years of age was seen in 41% of patients while 18% were alive at seven years. There was a significant difference between the NDUFS1 and NDUFV1 groups for clinical onset and life-span. Mutations in NDUFS1 were linked to a worse clinical course with earlier onset and earlier death. Conclusions: Genotype-phenotype correlations in patients with mutations affecting the genes that encode the electron input module of complex I vary, but patients with NDUFS1 mutation tend to have a worse clinical course than patients with NDUFV1 mutation. Identifying the mutations is of importance for accurate prognostic information and genetic counseling.
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| 7. |
- Schöll, Michael, 1980, et al.
(författare)
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Biomarkers for tau pathology.
- 2019
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Ingår i: Molecular and cellular neurosciences. - : Elsevier BV. - 1095-9327 .- 1044-7431. ; 97, s. 18-33
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Forskningsöversikt (refereegranskat)abstract
- The aggregation of fibrils of hyperphosphorylated and C-terminally truncated microtubule-associated tau protein characterizes 80% of all dementia disorders, the most common neurodegenerative disorders. These so-called tauopathies are hitherto not curable and their diagnosis, especially at early disease stages, has traditionally proven difficult. A keystone in the diagnosis of tauopathies was the development of methods to assess levels of tau protein in vivo in cerebrospinal fluid, which has significantly improved our knowledge about these conditions. Tau proteins have also been measured in blood, but the importance of tau-related changes in blood is still unclear. The recent addition of positron emission tomography ligands to visualize, map and quantify tau pathology has further contributed with information about the temporal and spatial characteristics of tau accumulation in the living brain. Together, the measurement of tau with fluid biomarkers and positron emission tomography constitutes the basis for a highly active field of research. This review describes the current state of biomarkers for tau biomarkers derived from neuroimaging and from the analysis of bodily fluids and their roles in the detection, diagnosis and prognosis of tau-associated neurodegenerative disorders, as well as their associations with neuropathological findings, and aims to provide a perspective on how these biomarkers might be employed prospectively in research and clinical settings.
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| 8. |
- Ali, Muhaddisa Barat, 1986, et al.
(författare)
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Multi-stream Convolutional Autoencoder and 2D Generative Adversarial Network for Glioma Classification
- 2019
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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
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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. |
- Borrelli, Pablo, et al.
(författare)
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Artificial intelligence-based detection of lymph node metastases by PET/CT predicts prostate cancer-specific survival
- 2021
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Ingår i: Clinical Physiology and Functional Imaging. - : Wiley. - 1475-0961 .- 1475-097X. ; 41:1, s. 62-67
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Tidskriftsartikel (refereegranskat)abstract
- Introduction Lymph node metastases are a key prognostic factor in prostate cancer (PCa), but detecting lymph node lesions from PET/CT images is a subjective process resulting in inter-reader variability. Artificial intelligence (AI)-based methods can provide an objective image analysis. We aimed at developing and validating an AI-based tool for detection of lymph node lesions. Methods A group of 399 patients with biopsy-proven PCa who had undergone(18)F-choline PET/CT for staging prior to treatment were used to train (n = 319) and test (n = 80) the AI-based tool. The tool consisted of convolutional neural networks using complete PET/CT scans as inputs. In the test set, the AI-based lymph node detections were compared to those of two independent readers. The association with PCa-specific survival was investigated. Results The AI-based tool detected more lymph node lesions than Reader B (98 vs. 87/117;p = .045) using Reader A as reference. AI-based tool and Reader A showed similar performance (90 vs. 87/111;p = .63) using Reader B as reference. The number of lymph node lesions detected by the AI-based tool, PSA, and curative treatment was significantly associated with PCa-specific survival. Conclusion This study shows the feasibility of using an AI-based tool for automated and objective interpretation of PET/CT images that can provide assessments of lymph node lesions comparable with that of experienced readers and prognostic information in PCa patients.
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