Title Domain adaptation and representation transfer, and distributed and collaborative learning : second MICCAI Workshop, DART 2020, and first MICCAI Workshop, DCL 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings / Shadi Albarqouni, Spyridon Bakas, Konstantinos Kamnitsas, M. Jorge Cardoso, Bennett Landman, Wenqi Li, Fausto Milletari, Nicola Rieke, Holger Roth, Daguang Xu, Ziyue Xu (eds.)

Published Cham, Switzerland : Springer, 2020

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Description 1 online resource Series Lecture notes in computer science ; 12444 LNCS sublibrary, SL 6, Image processing, computer vision, pattern recognition, and graphics Lecture notes in computer science ; 12444. LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics. Contents Intro -- Preface DART 2020 -- Preface DCL 2020 -- Organization -- Contents -- DART 2020 -- UNet++: A Data-Driven Neural Network Architecture for Medical Image Segmentation -- 1 Introduction -- 2 Datasets -- 3 Method -- 3.1 Pruning Decoding Blocks -- 3.2 UNet++C -- 3.3 Implementation and Evaluation -- 4 Results and Discussion -- 5 Conclusion -- References -- DAPR-Net: Domain Adaptive Predicting-Refinement Network for Retinal Vessel Segmentation -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Preprocessing Operations -- 3.2 Network Architecture -- 4 Experiments and Results 4.1 Datasets -- 4.2 Baselines and Evaluation Scenarios -- 4.3 Training Strategy -- 4.4 Ablation Study -- 4.5 Results -- 5 Conclusion -- References -- Augmented Radiology: Patient-Wise Feature Transfer Model for Glioma Grading -- Abstract -- 1 Introduction -- 2 Materials and Methodology -- 2.1 Datasets and Preprocessing -- 2.2 Baseline Radiological Model -- 2.3 Proposed Method: Augmented Radiological Model -- 3 Experiments and Results -- 3.1 Training/Evaluation Details -- 4 Conclusion -- References Attention-Guided Deep Domain Adaptation for Brain Dementia Identification with Multi-site Neuroimaging Data -- 1 Introduction -- 2 Methodology -- 2.1 Problem Definition -- 2.2 Proposed Attention-Guided Deep Domain Adaptation (AD2A) -- 3 Experiments -- 4 Conclusion -- References -- Registration of Histopathology Images Using Self Supervised Fine Grained Feature Maps -- 1 Introduction -- 1.1 Contributions -- 2 Method -- 2.1 Self Supervised Segmentation Feature Maps -- 2.2 Registration Using Segmentation Maps -- 3 Experimental Results -- 3.1 Implementation and Dataset Details 3.2 ANHIR Registration Results -- 3.3 Brain Image Registration -- 4 Conclusion -- References -- Cross-Modality Segmentation by Self-supervised Semantic Alignment in Disentangled Content Space -- 1 Introduction -- 2 Methodology -- 2.1 Problem Formulation -- 2.2 Overall Framework -- 2.3 Disentanglement Learning Module -- 2.4 Self-supervision Module -- 2.5 Segmentation Module -- 2.6 Implementation -- 3 Experiments -- 3.1 Datasets and Evaluation Metric -- 3.2 Experiment Settings -- 3.3 Results and Analysis -- 4 Conclusions -- References Semi-supervised Pathology Segmentation with Disentangled Representations -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Pathology Disentanglement -- 3.2 APD-Net Architecture -- 3.3 Individual Training Losses -- 3.4 Joint Optimization with Teacher-Forcing Training Strategy -- 4 Experiments -- 4.1 Results and Discussion -- 5 Conclusions and Future Work -- References -- Domain Generalizer: A Few-Shot Meta Learning Framework for Domain Generalization in Medical Imaging -- 1 Introduction and Background -- 2 Methodology -- 3 Experiments -- 3.1 Databases -- 3.2 Experimental Setup Summary This book constitutes the refereed proceedings of the Second MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2020, and the First MICCAI Workshop on Distributed and Collaborative Learning, DCL 2020, held in conjunction with MICCAI 2020 in October 2020. The conference was planned to take place in Lima, Peru, but changed to an online format due to the Coronavirus pandemic. For DART 2020, 12 full papers were accepted from 18 submissions. They deal with methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical settings by making them robust and consistent across different domains. For DCL 2020, the 8 papers included in this book were accepted from a total of 12 submissions. They focus on the comparison, evaluation and discussion of methodological advancement and practical ideas about machine learning applied to problems where data cannot be stored in centralized databases; where information privacy is a priority; where it is necessary to deliver strong guarantees on the amount and nature of private information that may be revealed by the model as a result of training; and where it's necessary to orchestrate, manage and direct clusters of nodes participating in the same learning task Notes International conference proceedings Includes author index Online resource; title from PDF title page (SpringerLink, viewed November 12, 2020) Subject Diagnostic imaging -- Data processing -- Congresses Diagnostic imaging -- Data processing Genre/Form proceedings (reports) Conference papers and proceedings Conference papers and proceedings. Actes de congrès. Form Electronic book Author Albarqouni, Shadi Bakas, Spyridon Kamnitsas, Konstantinos Cardoso, M. Jorge. Landman, Bennett Li, Wenqi Milletari, Fausto. Rieke, Nicola Roth, Holger Xu, Daguang Xu, Ziyue DCL (Workshop) (1st : 2020 : Lima, Peru) International Conference on Medical Image Computing and Computer-Assisted Intervention (23rd : 2020 : Online) ISBN 9783030605483 3030605485

Other Titles DART 2020 DCL 2020

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