Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
212
Utgivningsdatum
2020-09-26
Upplaga
1st ed. 2020
Förlag
Springer Nature Switzerland AG
Medarbetare
Albarqouni, Shadi (ed.), Xu, Daguang (ed.), Li, Wenqi (ed.), Bakas, Spyridon (ed.), Kamnitsas, Konstantinos (ed.), Cardoso, M. Jorge (ed.), Landman, Bennett (ed.), Xu, Ziyue (ed.), Milletari, Fausto (ed.), Rieke, Nicola (ed.), Roth, Holger (ed.)
Illustrationer
67 Illustrations, color; 19 Illustrations, black and white; XIII, 212 p. 86 illus., 67 illus. in col
Dimensioner
234 x 156 x 12 mm
Vikt
327 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783030605476

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 48, 2020, Proceedings

Häftad,  Engelska, 2020-09-26
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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.
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Innehållsförteckning

a-Unet++:A Data-driven Neural Network Architecture for Medical Image Segmentation.- DAPR-Net: Domain Adaptive Predicting-refinement Network for Retinal Vessel Segmentation.- Augmented Radiology: Patient-wise Feature Transfer Model for Glioma Grading.- Attention-Guided Deep Domain Adaptation for Brain Dementia Identication with Multi-Site Neuroimaging Data.- Registration of Histopathology Images Using Self Supervised Fine Grained Feature Maps.- Cross-Modality Segmentation by Self-Supervised Semantic Alignment in Disentangled Content Space.- Semi-supervised Pathology Segmentation with Disentangled Representations.- Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical Imaging.- Parts2Whole: Self-supervised Contrastive Learning via Reconstruction.- Cross-View Label Transfer in Knee MR Segmentation Using Iterative Context Learning.- Continual Class Incremental Learning for CT Thoracic Segmentation.- First U-Net Layers Contain More Domain SpecificInformation Than The Last Ones.- Siloed Federated Learning for Multi-Centric Histopathology Datasets.- On the Fairness of Privacy-Preserving Representations in Medical Applications.- Inverse Distance Aggregation for Federated Learning with Non-IID Data.- Weight Erosion: an Update Aggregation Scheme for Personalized Collaborative Machine Learning.- Federated Gradient Averaging for Multi-Site Training with Momentum-Based Optimizers.- Federated Learning for Breast Density Classification: A Real-World Implementation.- Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning.- Fed-BioMed: A general open-source frontend framework for federated learning in healthcare.