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
1. Auflage 2022
ISBN: 978-3-031-18522-9
Verlag: Springer Nature Switzerland

Buch, Englisch, Band 13573, 204 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 341 g

Reihe: Lecture Notes in Computer Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Albarqouni, Shadi

Rieke, Nicola

Bakas, Spyridon

Roth, Holger

Bano, Sophia

Sheet, Debdoot

Cardoso, M. Jorge

Li, Xiaoxiao

Khanal, Bishesh

Landman, Bennett

Qin, Chen

Rekik, Islem

Xu, Daguang

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision

Inhaltsverzeichnis

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.