Download or read book Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation written by Tanya Nair and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "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." --

Download or read book Automatic methods for multiple sclerosis new lesions detection and segmentation written by Olivier Commowick and published by Frontiers Media SA. This book was released on 2023-04-11 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Deep learning Convolutional Neural Network Framework for Mulitple Sclerosis Lesion Detection and Segmentation in Patient Brain Images written by Maor Zaltzhendler and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This thesis presents a convolutional neural network (CNN) based approach for detection and segmentation of Multiple Sclerosis lesions in brain magnetic resonance imaging (MRI). Automated pathology segmentation was presented in literature, starting from the early 1990s, and although reported to be a challenging task, could be highly beneficial for clinical trials labeling where large amounts of images are at hand. Robust detection of such pathology is still an open problem, and is prone to variabilities in: image non-uniformity, intensity distributions, acquisition artifacts, brain-structures, patients, scanners, configurations and sites. A CNN-based approach is proposed due to its recently reported high quality and generalization properties for computer vision tasks, providing a high degree of invariance and taking spatial correlation within the image structure into account. In order to address the task using both local and context-related information, a multi-scale approach is suggested, integrating the accuracy of several CNNs within a hierarchical framework for pathology segmentation. The presented model is general, and could be used for other pathology detection and segmentation contexts that require object delineation and classification in 3D magnetic resonance imaging. Several different architectures and experiments are presented throughout the document, while providing benchmarks and qualitative views over their results. Additional contributions of this thesis include: (a) learning CNN-based brain-features, evaluating their discriminative power, and observe appearance and constancy, (b) develop a general approach for MRI segmentation, while naturally incorporating the full 3D neighbourhood information rather than using 2D or augmented-2D with consecutive slices information. A comprehensive set of experiments is provided throughout this thesis, and performed over two different multi-site large scale proprietary clinical trials that were made available for this research. First, the method was configured and tested over the first clinical trial only. Once the hyper-parameters were set, no further tuning was allowed and the architecture was tested over the second clinical trial, which is much larger, and showed similar performance. The results of the method over this data yielded sensitivity values of up to 0.68, and Dice scores up to 0.59. The method achieved even higher metric scores of 0.86-1.00 true-positive rates when considering only larger lesions. The experiments performed show comparable performance to previously reported results from the literature over the same dataset. The data-driven features are presented, and shown to capture brain structures that lead to MS lesion discrimination both qualitatively and quantitatively." --

Download or read book Deep Learning Methods for Automated Detection of New Multiple Sclerosis Lesions in Longitudinal Magnetic Resonance Images written by Mostafa Salem and published by . This book was released on 2020 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Brainlesion Glioma Multiple Sclerosis Stroke and Traumatic Brain Injuries written by Alessandro Crimi and published by Springer Nature. This book was released on 2021-03-26 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book MEDICON 23 and CMBEBIH 23 written by Almir Badnjević and published by Springer Nature. This book was released on 2024-01-03 with total page 923 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Engineering Deep Learning Systems for Robust and Accurate Focal Pathology Segmentation and Detection written by Brennan Nichyporuk and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This thesis presents a deep learning framework, along with key insights and design decisions, for the problem of Multiple Sclerosis (MS) lesion segmentation and detection. Our approach, inspired by nnU-Net ('No-New-Net'), confirms that a baseline UNet, properly configured and optimized, can achieve state-of-the-art performance on medical image segmentation tasks. Our approach shows significant performance gains over previously published results on the same dataset. Next, we examine the segmentation-detection tradeoff that exists when segmenting MS lesions of different sizes. Specifically, in cases where there is a mix of small and large lesions, standard binary cross entropy loss will result in better segmentation of large lesions at the expense of missing small ones. We propose Lesion Size Reweighing (LSR) to reweigh the loss function such that good detection performance does not come at the cost of segmentation quality. Experiments show significant improvements in small lesion detection performance while maintaining segmentation accuracy. Finally, we examine the role of dataset bias in the context of aggregated datasets, proposing a generalized affine conditioning framework to learn and account for complex cohort biases across multi-source datasets"--

Download or read book Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image Based Procedures written by Hayit Greenspan and published by Springer Nature. This book was released on 2019-10-10 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2019 written by Dinggang Shen and published by Springer Nature. This book was released on 2019-10-12 with total page 860 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019. The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: optical imaging; endoscopy; microscopy. Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression. Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging. Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis. Part V: computer assisted interventions; MIC meets CAI. Part VI: computed tomography; X-ray imaging.

Download or read book Computer Vision ECCV 2022 Workshops written by Leonid Karlinsky and published by Springer Nature. This book was released on 2023-02-17 with total page 797 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online. The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.

Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2022 written by Linwei Wang and published by Springer Nature. This book was released on 2022-09-15 with total page 774 pages. Available in PDF, EPUB and Kindle. Book excerpt: The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022. The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology; Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging; Part III: Breast imaging; colonoscopy; computer aided diagnosis; Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I; Part V: Image segmentation II; integration of imaging with non-imaging biomarkers; Part VI: Image registration; image reconstruction; Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization; Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies.

Download or read book Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Graphs in Biomedical Image Analysis written by Carole H. Sudre and published by Springer Nature. This book was released on 2020-10-05 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Uncertainty for Safe Utilization of Machine Learning in Medical Imaging written by Carole H. Sudre and published by Springer Nature. This book was released on 2022-09-17 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2021 written by Marleen de Bruijne and published by Springer Nature. This book was released on 2021-09-23 with total page 676 pages. Available in PDF, EPUB and Kindle. Book excerpt: The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging – others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually.

Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2023 written by Hayit Greenspan and published by Springer Nature. This book was released on 2023-09-30 with total page 844 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023. The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections: Part I: Machine learning with limited supervision and machine learning – transfer learning; Part II: Machine learning – learning strategies; machine learning – explainability, bias, and uncertainty; Part III: Machine learning – explainability, bias and uncertainty; image segmentation; Part IV: Image segmentation; Part V: Computer-aided diagnosis; Part VI: Computer-aided diagnosis; computational pathology; Part VII: Clinical applications – abdomen; clinical applications – breast; clinical applications – cardiac; clinical applications – dermatology; clinical applications – fetal imaging; clinical applications – lung; clinical applications – musculoskeletal; clinical applications – oncology; clinical applications – ophthalmology; clinical applications – vascular; Part VIII: Clinical applications – neuroimaging; microscopy; Part IX: Image-guided intervention, surgical planning, and data science; Part X: Image reconstruction and image registration.

Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2018 written by Alejandro F. Frangi and published by Springer. This book was released on 2018-09-13 with total page 918 pages. Available in PDF, EPUB and Kindle. Book excerpt: The four-volume set LNCS 11070, 11071, 11072, and 11073 constitutes the refereed proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, held in Granada, Spain, in September 2018. The 373 revised full papers presented were carefully reviewed and selected from 1068 submissions in a double-blind review process. The papers have been organized in the following topical sections: Part I: Image Quality and Artefacts; Image Reconstruction Methods; Machine Learning in Medical Imaging; Statistical Analysis for Medical Imaging; Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications; Histology Applications; Microscopy Applications; Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications; Lung Imaging Applications; Breast Imaging Applications; Other Abdominal Applications. Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging; Diffusion Weighted Imaging; Functional MRI; Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging; Brain Segmentation Methods. Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery; Surgical Planning, Simulation and Work Flow Analysis; Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications; Multi-Organ Segmentation; Abdominal Segmentation Methods; Cardiac Segmentation Methods; Chest, Lung and Spine Segmentation; Other Segmentation Applications.

Download or read book Deep Learning for Medical Image Analysis written by S. Kevin Zhou and published by Academic Press. This book was released on 2023-12-01 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis. · Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache