Medical Imaging Benchmarks
Cross-source consensus on Medical Imaging Benchmarks from 1 sources and 4 claims.
1 sources · 4 claims
Uses
Preparation
Risks & contraindications
Highlighted claims
- The experiments used OASIS, PathMNIST, and OrganAMNIST as medical imaging benchmarks. — Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging
- Client data were partitioned with a Dirichlet distribution to simulate cross-silo heterogeneity. — Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging
- The model was a 4-layer CNN designed for 128 x 128 medical images and computational efficiency. — Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging
- Strong non-IID heterogeneity made unlearning harder but retained substantial utility. — Asynchronous Federated Unlearning with Invariance Calibration for Medical Imaging