4 citations · 7 across the 9 of their papers we have counts for
5 papers · 1 filter
Sample selection with noise rate estimation in noise learning of medical image analysis
Maolin Li, Giacomo Tarroni
In the field of medical image analysis, deep learning models have demonstrated remarkable success in enhancing diagnostic accuracy and efficiency. However, the reliability of these…
Contribution Evaluation in Federated Learning: Examining Current Approaches
Vasilis Siomos, Jonathan Passerat-Palmbach
Federated Learning (FL) has seen increasing interest in cases where entities want to collaboratively train models while maintaining privacy and governance over their data. In FL, c…
DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection
Sergio Naval Marimont, Matthew Baugh, Vasilis Siomos +3
Unsupervised Anomaly Detection (UAD) techniques aim to identify and localize anomalies without relying on annotations, only leveraging a model trained on a dataset known to be free…
ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual Classification
Vasilis Siomos, Sergio Naval-Marimont, Jonathan Passerat-Palmbach +1
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…
MIM-OOD: Generative Masked Image Modelling for Out-of-Distribution Detection in Medical Images
Sergio Naval Marimont, Vasilis Siomos, Giacomo Tarroni
Unsupervised Out-of-Distribution (OOD) detection consists in identifying anomalous regions in images leveraging only models trained on images of healthy anatomy. An established app…