AI RESEARCH
Learning Coarse-to-Fine Osteoarthritis Representations under Noisy Hierarchical Labels
arXiv CS.CV
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ArXi:2605.00718v1 Announce Type: new Knee osteoarthritis (OA) assessment involves a natural but often underused label hierarchy: a coarse binary OA decision and a fine-grained Kellgren--Lawrence (KL) severity grade. Existing deep learning studies commonly treat these targets as separate classification problems, either reducing OA assessment to disease presence or directly optimizing noisy ordinal KL labels. In this work, we ask whether this clinical hierarchy can serve as a representation-level supervisory prior. Rather than.