MobenFL is the broadest federated medical-imaging benchmark to date, pairing 20 algorithms with 22 multi-organ datasets and adding efficiency plus privacy metrics.
author Patravali, P
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H-SemiS decomposes multi-class KOA severity grading into binary sub-tasks in a semi-supervised setup with self-supervision and quantum-inspired mixing, outperforming baselines on two multi-class and two binary datasets.
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Benchmark Evaluation of Feredated Learning on Multi-organ Images
MobenFL is the broadest federated medical-imaging benchmark to date, pairing 20 algorithms with 22 multi-organ datasets and adding efficiency plus privacy metrics.
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H-SemiS: Hierarchical Fusion of Semi and Self-Supervised Learning for Knee Osteoarthritis Severity Grading
H-SemiS decomposes multi-class KOA severity grading into binary sub-tasks in a semi-supervised setup with self-supervision and quantum-inspired mixing, outperforming baselines on two multi-class and two binary datasets.