Learning Coarse-to-Fine Osteoarthritis Representations under Noisy Hierarchical Labels

Abstract

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 retains stronger single-task performance. Representation analysis further shows an architecture-dependent effect: Dual strengthens label-aligned latent organization for ResNet3D and M3T, while nnMamba retains stronger alignment under single-task supervision. For the responsive backbones, Dual also produces descriptively higher saliency overlap with cartilage. These results show that coarse-to-fine supervision can reshape disease representations under noisy hierarchical labels, with benefits that depend on backbone architecture. Code is available at https://github.com/jukieCheung/coarse2fine-oa-mri.