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Author

M. Linguraru

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#federated learning Preprint Oct 2026

Fed-ADApt: Federated Anytime Depth Adaptation for Resource-Aware Medical Image Segmentation

Federated learning (FL) enables collaborative training of medical image segmentation models without sharing raw patient data, yet existing approaches assume a homogeneous compute budget across institutions, limiting participation of low-resource sites. We propose Fed-ADApt, a depth-adaptive federated framework for UNet...

Abhijeet Parida, Zhi-Fan Jiang, Pooneh Roshanitabrizi et al. · 0 citations
Open access Aug 2026

Improved Deep Learning Segmentation of Pediatric Diffuse Midline Gliomas After Treatment.

Training DMG segmentation models with post-treatment scans substantially improves performance in longitudinal clinical trial imaging, enabling more accurate volumetric tracking and response assessment.

John Zielke, F. Mussa, A. Zapaishchykova et al. · 0 citations

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