no code implementations • 23 Dec 2023 • Maolin Li, Giacomo Tarroni, Vasilis Siomos
Deep learning techniques have demonstrated remarkable success in the field of medical image analysis.
no code implementations • 26 Nov 2023 • Sergio Naval Marimont, Matthew Baugh, Vasilis Siomos, Christos Tzelepis, Bernhard Kainz, Giacomo Tarroni
Such a score function is potentially relevant for UAD, since $\nabla_x \log p(x)$ is itself a pixel-wise anomaly score.
1 code implementation • 24 Nov 2023 • Vasilis Siomos, Sergio Naval-Marimont, Jonathan Passerat-Palmbach, Giacomo Tarroni
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.
no code implementations • 16 Nov 2023 • 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.
no code implementations • 27 Jul 2023 • 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.