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Found 5 papers, 1 papers with code
Sample selection with noise rate estimation in noise learning of medical image analysis
Deep learning techniques have demonstrated remarkable success in the field of medical image analysis.
DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection
Such a score function is potentially relevant for UAD, since $\nabla_x \log p(x)$ is itself a pixel-wise anomaly score.
ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual Classification
Federated Learning (FL) is a collaborative training paradigm that allows for privacy-preserving learning of cross-institutional models by eliminating the exchange of sensitive data and instead relying on the exchange of model parameters between the clients and a server.
Contribution Evaluation in Federated Learning: Examining Current Approaches
Federated Learning (FL) has seen increasing interest in cases where entities want to collaboratively train models while maintaining privacy and governance over their data.
MIM-OOD: Generative Masked Image Modelling for Out-of-Distribution Detection in Medical Images
Unsupervised Out-of-Distribution (OOD) detection consists in identifying anomalous regions in images leveraging only models trained on images of healthy anatomy.
