From the 1 of 4 linked papers with an AI index.
4 papers
Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging
Shengchao Chen, Ting Shu
Federated learning enables medical-imaging models to be trained across hospitals, and privacy law, most explicitly the GDPR ``right to be forgotten'', turns removing a hospital's,…
FM: Unified Federated Foundation Models for Heterogeneous Multimodal Medical Imaging
Shengchao Chen, Ting Shu
The paper introduces FM², a federated learning framework that trains a unified foundation model for heterogeneous multimodal medical images while preserving privacy, using dual Mix…
Learning Design Skills as Memory Policies for Agentic Photonic Inverse Design
Shengchao Chen, Ting Shu, Sufen Ren
Photonic crystal fiber (PCF) inverse design remains challenging because candidate geometries must satisfy coupled optical targets under expensive electromagnetic simulation. Existi…
Boosting Sclera Segmentation through Semi-supervised Learning with Fewer Labels
Guanjun Wang, Lu Wang, Ning Niu +4
Sclera segmentation is crucial for developing automatic eye-related medical computer-aided diagnostic systems, as well as for personal identification and verification, because the…