Exploring Uncertainty Measures In Deep Networks For Multiple Sclerosis Lesion Detection And Segmentation
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Exploring Uncertainty Measures In Deep Networks For Multiple Sclerosis Lesion Detection And Segmentation
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Author : Tanya Nair
language : en
Publisher:
Release Date : 2018
Exploring Uncertainty Measures In Deep Networks For Multiple Sclerosis Lesion Detection And Segmentation written by Tanya Nair and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
"This thesis presents the first exploration of multiple uncertainty estimates based on Monte Carlo (MC) dropout sampling in the context of deep networks for lesion detection and segmentation in medical images. Recently, deep learning frameworks have been shown to outperform traditional machine learning approaches to automated segmentation on a variety of public, medical-image challenge datasets, particularly for large pathologies. However, in the context of diseases such as Multiple Sclerosis (MS), monitoring all the focal lesions visible on MRI sequences, even the very small ones, is essential for disease staging, prognosis, and evaluating treatment efficacy, and deep learning models continue to underperform traditional machine learning approaches when it comes to small lesion segmentation. This, coupled with the deterministic output predictions made by deep learning models, continues to hinder their adoption into clinical routines. In order to address these barriers to deep learning's adoption in medical imaging, an approach that provides uncertainty estimates for a deep learning model's predictions is suggested, which would permit the subsequent revision by clinicians. While recent work in another domain shows the early, promising use of one uncertainty estimate in deep networks, there are several different measures of uncertainty can be calculated, and a thorough investigation of these in a clinically relevant context is lacking. The presented methodology is a 3D MS lesion segmentation CNN, augmented to provide four different voxel-based uncertainty measures based on Monte Carlo dropout. Lesion candidates are obtained from voxel-wise predictions of the network in a standard approach and a method is presented in the thesis to combine the voxel-wise uncertainties into lesion-level uncertainties. To evaluate the usefulness of the different measures, a method is presented to filter out either (a) voxels or (b) lesions for two separate comprehensive analyses such that the most uncertain regions are removed from the performance analysis of voxel segmentation or lesion detection. This filtering approach is contrasted against a standard deep learning approach of filtering predictions based on the non-probabilistic sigmoid or softmax output. The comprehensive experiments comparing the different uncertainties and their usefulness are performed with a proprietary, large-scale, multi-site, multi-scanner, clinical MS dataset. The results of the method over this data determine that across all measures, filtering out the most uncertain lesions greatly improves the lesion detection performance. Small lesions, which make up 40% of the dataset, are found to be the most uncertain and are shown to be main driver of the overall improvement when using uncertainty filtering. Even when excluding just 2% of all lesions, uncertainty based filtering improves the lesion-wise True Positive Rate from 0.75 to 0.8 at a lesion-wise False Detection Rate of 0.2 on remaining predictions. Additionally, the uncertainty-based filtering consistently performs better than sigmoid filtering. Reporting these results across the range of experiments serves as a reference to future researchers who want to apply deep learning methods in medical imaging and other safety-critical applications." --
Mechanisms Of Disease Pathogenesis In Multiple Sclerosis
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Author : Francisco Javier Quintana
language : en
Publisher: Elsevier
Release Date : 2024-06-28
Mechanisms Of Disease Pathogenesis In Multiple Sclerosis written by Francisco Javier Quintana and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-28 with Medical categories.
Mechanisms of Disease Pathogenesis in Multiple Sclerosis summarizes our current understanding on MS and its clinical features and monitoring with available biomarkers, focusing on mechanisms that drive disease pathogenesis and their control by genetic, environmental factors and novel therapies for disease management. The book is written for neurologists, neuroimmunologists and clinical, translational and basic researchers interested in mechanisms of neurodegeneration. Multiple Sclerosis (MS) is an autoimmune disease which targets the central nervous system (CNS). It is the most common cause of non-traumatic neurological disability in young adults with a prevalence of 1 in 1000 and increasing, hence the importance of this book. Summarizes our current understanding of Multiple Sclerosis Discusses clinical features and available biomarker monitoring Focuses on mechanisms that drive disease pathogenesis
Uncertainty For Safe Utilization Of Machine Learning In Medical Imaging And Clinical Image Based Procedures
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Author : Hayit Greenspan
language : en
Publisher: Springer Nature
Release Date : 2019-10-10
Uncertainty For Safe Utilization Of Machine Learning In Medical Imaging And Clinical Image Based Procedures written by Hayit Greenspan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-10 with Computers categories.
This book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For UNSURE 2019, 8 papers from 15 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. CLIP 2019 accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.
Brainlesion Glioma Multiple Sclerosis Stroke And Traumatic Brain Injuries
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Author : Alessandro Crimi
language : en
Publisher: Springer Nature
Release Date : 2021-03-26
Brainlesion Glioma Multiple Sclerosis Stroke And Traumatic Brain Injuries written by Alessandro Crimi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-26 with Computers categories.
This two-volume set LNCS 12658 and 12659 constitutes the thoroughly refereed proceedings of the 6th International MICCAI Brainlesion Workshop, BrainLes 2020, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, and the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge. These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in Lima, Peru, in October 2020.* The revised selected papers presented in these volumes were organized in the following topical sections: brain lesion image analysis (16 selected papers from 21 submissions); brain tumor image segmentation (69 selected papers from 75 submissions); and computational precision medicine: radiology-pathology challenge on brain tumor classification (6 selected papers from 6 submissions). *The workshop and challenges were held virtually.
Uncertainty For Safe Utilization Of Machine Learning In Medical Imaging
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Author : Carole H. Sudre
language : en
Publisher: Springer Nature
Release Date : 2022-09-17
Uncertainty For Safe Utilization Of Machine Learning In Medical Imaging written by Carole H. Sudre and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-17 with Computers categories.
This book constitutes the refereed proceedings of the Fourth Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2022, held in conjunction with MICCAI 2022. The conference was hybrid event held from Singapore. For this workshop, 13 papers from 22 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world.
Medicon 23 And Cmbebih 23
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Author : Almir Badnjević
language : en
Publisher: Springer Nature
Release Date : 2024-01-03
Medicon 23 And Cmbebih 23 written by Almir Badnjević and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-03 with Technology & Engineering categories.
This book presents cutting-edge research and developments in the broad field of medical, biological engineering and computing. This is the first volume of the joint proceedings of the Mediterranean Conference on Medical and Biological Engineering and Computing (MEDICON) and the International Conference on Medical and Biological Engineering (CMBEBIH), which were held together on September 14-16, 2023, in Sarajevo, Bosnia and Herzegovina. Contributions report on advances in biomedical signal processing and bioimaging, medical physics, and pharmaceutical engineering. Further, they cover applications of artificial intelligence and machine learning in healthcare.
Deep Learning Methods For Automated Detection Of New Multiple Sclerosis Lesions In Longitudinal Magnetic Resonance Images
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Author : Mostafa Salem
language : en
Publisher:
Release Date : 2020
Deep Learning Methods For Automated Detection Of New Multiple Sclerosis Lesions In Longitudinal Magnetic Resonance Images written by Mostafa Salem and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
This thesis is focused on developing novel and fully automated methods for the detection of new multiple sclerosis (MS) lesions inlongitudinal brain magnetic resonance imaging (MRI). First, we proposed a fully automated logistic regression-based framework forthe detection and segmentation of new T2-w lesions. The framework was based on intensity subtraction and deformation field (DF).Second, we proposed a fully convolutional neural network (FCNN) approach to detect new T2-w lesions in longitudinal brain MRimages. The model was trained end-to-end and simultaneously learned both the DFs and the new T2-w lesions. Finally, weproposed a deep learning-based approach for MS lesion synthesis to improve the lesion detection and segmentation performancein both cross-sectional and longitudinal analysis.
Uncertainty For Safe Utilization Of Machine Learning In Medical Imaging And Graphs In Biomedical Image Analysis
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Author : Carole H. Sudre
language : en
Publisher: Springer Nature
Release Date : 2020-10-05
Uncertainty For Safe Utilization Of Machine Learning In Medical Imaging And Graphs In Biomedical Image Analysis written by Carole H. Sudre and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-05 with Computers categories.
This book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the Third International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. For UNSURE 2020, 10 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts.
Computer Vision Eccv 2022
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Author : Shai Avidan
language : en
Publisher: Springer Nature
Release Date : 2022-11-10
Computer Vision Eccv 2022 written by Shai Avidan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-10 with Computers categories.
The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Automatic Methods For Multiple Sclerosis New Lesions Detection And Segmentation
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Author : Olivier Commowick
language : en
Publisher: Frontiers Media SA
Release Date : 2023-04-11
Automatic Methods For Multiple Sclerosis New Lesions Detection And Segmentation written by Olivier Commowick and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-11 with Science categories.