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Distributed, collaborative, and federated learning, and affordable AI and healthcare for resource diverse global healththird MICCAI Workshop, DeCaF 2022 and second MICCAI Workshop, FAIR 2022, held in conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022 : proceedings /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Distributed, collaborative, and federated learning, and affordable AI and healthcare for resource diverse global healthedited by Shadi Albarqouni ... [et al.].
其他題名:	third MICCAI Workshop, DeCaF 2022 and second MICCAI Workshop, FAIR 2022, held in conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022 : proceedings /
其他題名:	DeCaF 2022
其他作者:	Albarqouni, Shadi.
出版者:	Cham :Springer Nature Switzerland :2022.
面頁冊數:	xv, 204 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Diagnostic imagingCongresses.Data processing
電子資源:	https://doi.org/10.1007/978-3-031-18523-6
ISBN:	9783031185236$q(electronic bk.)

Distributed, collaborative, and federated learning, and affordable AI and healthcare for resource diverse global healththird MICCAI Workshop, DeCaF 2022 and second MICCAI Workshop, FAIR 2022, held in conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022 : proceedings /
Distributed, collaborative, and federated learning, and affordable AI and healthcare for resource diverse global healththird MICCAI Workshop, DeCaF 2022 and second MICCAI Workshop, FAIR 2022, held in conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022 : proceedings /[electronic resource] :DeCaF 2022edited by Shadi Albarqouni ... [et al.]. - Cham :Springer Nature Switzerland :2022. - xv, 204 p. :ill. (chiefly color), digital ;24 cm. - Lecture notes in computer science,135730302-9743 ;. - Lecture notes in computer science ;4891..

Distributed, Collaborative, and Federated Learning -- Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation -- FedAP: Adaptive Personalization in Federated Learning for Non-IID Data Data Stealing Attack on Medical Images: Is it Safe to Export Networks from Data Lakes? -- Data Stealing Attack on Medical Images: Is it Safe to Export Networks from Data Lakes? -- Can collaborative learning be private, robust and scalable? -- Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation -- Joint Multi Organ and Tumor Segmentation from Partial Labels using Federated Learning -- Fuh, Kensaku Mori, Weichung Wang, Holger R Roth GAN Latent Space Manipulation and Aggregation for Federated Learning in Medical Imaging -- A Specificity-Preserving Generative Model for Federated MRI Translation -- Content-Aware Differential Privacy with Conditional Invertible Neural Networks -- DeMed: A Novel and Efficient Decentralized Learning Framework for Medical Images Classification on Blockchain -- Cluster Based Secure Multi-Party Computation in Federated Learning for Histopathology Images -- Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling -- Towards Real-World Federated Learning in Medical Image Analysis Using Kaapana -- Towards Sparsified Federated Neuroimaging Models via Weight Pruning -- Affordable AI and Healthcare -- Enhancing Portable OCT Image Quality via GANs for AI-Based Eye Disease Detection -- Deep Learning-based Segmentation of Pleural Effusion From Ultrasound Using Coordinate Convolutions -- Verifiable and Energy Efficient Medical Image Analysis with Quantised Self-attentive Deep Neural Networks -- LRH-Net: A Multi-Level Knowledge Distillation Approach for Low-Resource Heart Network.

This book constitutes the refereed proceedings of the Third MICCAI Workshop on Distributed, Collaborative, and Federated Learning, DeCaF 2022, and the Second MICCAI Workshop on Affordable AI and Healthcare, FAIR 2022, held in conjunction with MICCAI 2022, in Singapore in September 2022. FAIR 2022 was held as a hybrid event. DeCaF 2022 accepted 14 papers from the 18 submissions received. The workshop aims at creating a scientific discussion focusing 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 or where information privacy is a priority. For FAIR 2022, 4 papers from 9 submissions were accepted for publication. The topics of the accepted submissions focus on deep ultrasound segmentation, portable OCT image quality enhancement, self-attention deep networks and knowledge distillation in low-regime setting.

ISBN: 9783031185236$q(electronic bk.)

Standard No.: 10.1007/978-3-031-18523-6doiSubjects--Topical Terms:

445765
Diagnostic imaging
--Data processing--Congresses.

LC Class. No.: TA1634 / .D57 2022

Dewey Class. No.: 616.0757

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