Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health : Third MICCAI Workshop, DeCaF 2022, and Second MICCAI Workshop, FAIR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18 and 22, 2022, Proceedings / edited by Shadi Albarqouni, Spyridon Bakas, Sophia Bano, M. Jorge Cardoso, Bishesh Khanal, Bennett Landman, Xiaoxiao Li, Chen Qin, Islem Rekik, Nicola Rieke, Holger Roth, Debdoot Sheet, Daguang Xu.

Format:

Book

Contributor:

Albarqouni, Shadi, editor.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 13573

Language:

English

Subjects (All):

Computer vision.

Artificial intelligence.

Computers.

Application software.

Computer Vision.

Artificial Intelligence.

Computing Milieux.

Computer and Information Systems Applications.

Local Subjects:

Computer Vision.

Artificial Intelligence.

Computing Milieux.

Computer and Information Systems Applications.

Physical Description:

1 online resource (215 pages)

Edition:

1st ed. 2022.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2022.

Summary:

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.

Contents:

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.

Notes:

Includes bibliographical references and index.

Other Format:

Print version: Albarqouni, Shadi Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health

ISBN:

9783031185236

3031185234