A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications

9 December 2024

Papers citing "A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications"

6 / 6 papers shown

Title
Benchmarking federated strategies in Peer-to-Peer Federated learning for biomedical data Jose L. Salmeron Irina Arévalo A. Ruiz-Celma OOD FedML 79 11 0 15 Feb 2024
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications Francesco Cremonesi Marc Vesin Sergen Cansiz Yannick Bouillard Irene Balelli ... B. Houis R. Modzelewski O. Humbert Melek Önen Marco Lorenzi FedML OOD 85 11 0 24 Apr 2023
The More, the Better? A Study on Collaborative Machine Learning for DGA Detection Arthur Drichel Benedikt Holmes Justus von Brandt Ulrike Meyer 70 6 0 24 Sep 2021
The Future of Digital Health with Federated Learning Nicola Rieke Jonny Hancox Wenqi Li Fausto Milletari H. Roth ... Ronald M. Summers Andrew Trask Daguang Xu Maximilian Baust M. Jorge Cardoso OOD 174 1,690 0 18 Mar 2020
TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning Fernando Pérez-García Rachel Sparks Sébastien Ourselin MedIm LM&MA 129 357 0 09 Mar 2020
U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger Philipp Fischer Thomas Brox SSeg 3DV 229 74,467 0 18 May 2015