Author

M. Antonelli

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#artificial intelligence Preprint Oct 2026

MID: Mask-Image Distributional Divergence for Evaluating Medical Image Segmentation

This work introduces MID (Mask-Image Distributional Divergence), a distribution-level evaluation metric for medical image segmentation that compares the joint distribution of images and masks between a reference set and a set of predictions, without requiring sample-wise correspondence.

Vincenzo Marcianó, Xiao-Ming Zhang, Gianluca Guglielmo et al. · 0 citations

#machine learning Review Sep 2026

Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Study

FLIP (Federated Learning Interoperability Platform), an open-source, multi-application platform that makes FL training and evaluation repeatable and improve repeatability, auditability, and site-specific governance, is presented.

R. Garcia-Dias, A. Bagur, Chayanin Tangwiriyasakul et al. · 0 citations