Learning Coarse-to-Fine Osteoarthritis Representations under Noisy Hierarchical Labels
Knee osteoarthritis (OA) assessment contains a natural label hierarchy between binary disease status and Kellgren--Lawrence (KL) severity. We study whether supervision at these two granularities changes learned 3D MRI representations. A shared encoder with OA and KL prediction heads is evaluated under single-OA, single-KL, and dual-head training across ResNet3D, M3T, and nnMamba backbones. Evaluation combines predictive metrics with paired statistical comparisons under Benjamini--Hochberg FDR control, latent severity-axis geometry, and saliency--cartilage overlap. Dual supervision yields significant KL-grading gains for ResNet3D and a significant OA AUC gain for M3T, whereas nnMamba retai...
| Authors | Tongxu Zhang |
|---|---|
| Primary category | cs.CV |
| Published | 1 May 2026 |
| Last updated at source | 19 August 2026 |
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