東華大學圖書館 |

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 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Domain adaptation and representation transfer, and distributed and collaborative learning/ edited by Shadi Albarqouni ... [et al.].
Reminder of title:	second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings /
remainder title:	DART 2020
other author:	Albarqouni, Shadi.
corporate name:	DART (Workshop)
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xiii, 212 p. :ill., digital ;24 cm.
[NT 15003449]:	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 Specific Information 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.
Contained By:	Springer Nature eBook
Subject:	Diagnostic imaging - Congresses. - Data processing -
Online resource:	https://doi.org/10.1007/978-3-030-60548-3
ISBN:	9783030605483

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 /
Domain adaptation and representation transfer, and distributed and collaborative learningsecond MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings /[electronic resource] :DART 2020edited by Shadi Albarqouni ... [et al.]. - Cham :Springer International Publishing :2020. - xiii, 212 p. :ill., digital ;24 cm. - Lecture notes in computer science,124440302-9743 ;. - Lecture notes in computer science ;12444..

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 Specific Information 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.

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.

ISBN: 9783030605483

Standard No.: 10.1007/978-3-030-60548-3doiSubjects--Topical Terms:

893542
Diagnostic imaging
--Data processing--Congresses.

LC Class. No.: RC78.7.D53 / D37 2020

Dewey Class. No.: 616.0754

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 /
LDR:04471nmm a2200397 a 4500 001 2243616
003 DE-He213
005 20200925084753.0
006 m d
007 cr nn 008maaau
008 211207s2020 sz s 0 eng d
020 $a 9783030605483 $q (electronic bk.)
020 $a 9783030605476 $q (paper)
024 7 $a 10.1007/978-3-030-60548-3 $2 doi
035 $a 978-3-030-60548-3
040 $a GP $c GP
041 0 $a eng
050 4 $a RC78.7.D53 $b D37 2020
072 7 $a UYT $2 bicssc
072 7 $a COM012000 $2 bisacsh
072 7 $a UYT $2 thema
072 7 $a UYQV $2 thema
082 0 4 $a 616.0754 $2 23
090 $a RC78.7.D53 $b D226 2020
111 2 $a DART (Workshop) $n (2nd : $d 2020 : $c Lima, Peru) $3 3503827
245 1 0 $a Domain adaptation and representation transfer, and distributed and collaborative learning $h [electronic resource] : $b second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020 : proceedings / $c edited by Shadi Albarqouni ... [et al.].
246 3 $a DART 2020
246 3 $a DCL 2020
246 3 $a MICCAI 2020
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xiii, 212 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 12444
490 1 $a Image processing, computer vision, pattern recognition, and graphics
505 0 $a 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 Specific Information 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.
520 $a 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.
650 0 $a Diagnostic imaging $x Data processing $v Congresses. $3 893542
650 1 4 $a Image Processing and Computer Vision. $3 891070
650 2 4 $a Computer Appl. in Social and Behavioral Sciences. $3 892702
650 2 4 $a Computers and Education. $3 892757
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Information Systems Applications (incl. Internet) $3 1565452
700 1 $a Albarqouni, Shadi. $3 3503828
710 2 $a SpringerLink (Online service) $3 836513
711 2 $a DCL (Workshop) $n (1st : $d 2020 : $c Lima, Peru) $3 3503830
711 2 $a International Conference on Medical Image Computing and Computer-Assisted Intervention $n (23rd : $d 2020 : $c Lima, Peru) $3 3489508
773 0 $t Springer Nature eBook
830 0 $a Lecture notes in computer science ; $v 12444. $3 3503829
830 0 $a Image processing, computer vision, pattern recognition, and graphics. $3 3382509
856 4 0 $u https://doi.org/10.1007/978-3-030-60548-3
950 $a Computer Science (SpringerNature-11645)