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 introducing a complex architecture, we use a deliberately simple dual-head model with a shared encoder and two task-specific heads as a probe of hierarchical supervision. We compare single-OA, single-KL, and dual-head training across multiple 3D backbones under the same test protocol. Beyond standard classification metrics, we perform paired statistical comparisons, analyze latent severity-axis geometry, and examine saliency overlap with cartilage regions. The results show that dual-head supervision produces backbone-dependent gains, with clear improvements in KL-related metrics for selected backbones. More importantly, the gains are accompanied by a more ordered coarse-to-fine latent organization and, for responsive backbones, stronger anatomical alignment of saliency with cartilage. These findings suggest that even simple hierarchical dual-head supervision can reshape disease representations under noisy coarse/fine labels, providing a useful inductive bias for OA diagnosis and severity grading.

翻译：膝关节骨关节炎（OA）评估涉及一种自然但常被低估的标签层级结构：粗粒度的二元OA判定和细粒度的Kellgren-Lawrence（KL）严重程度等级。现有的深度学习研究通常将这些目标视为独立的分类问题，要么将OA评估简化为疾病存在性判断，要么直接优化含噪声的有序KL标签。本文探讨这一临床层级结构能否作为表示层面的监督先验。我们并未引入复杂架构，而是采用一个精心设计的简单双头模型（共享编码器加两个任务专用头）作为层级监督的探测工具。在相同测试协议下，我们针对多种3D骨干网络比较了单OA、单KL和双头训练。除标准分类指标外，我们进行了配对统计比较，分析了潜在严重性轴几何结构，并检查了与软骨区域的显著性重叠。结果表明，双头监督产生了依赖骨干网络的增益，在选定的骨干网络中KL相关指标显著提升。更重要的是，这种增益伴随着更有序的从粗到细潜在组织，对于响应性骨干网络，显著性在解剖学上与软骨区域更紧密对齐。这些发现表明，即使在噪声粗/细标签下，简单的层级双头监督也能重塑疾病表示，为OA诊断和严重程度分级提供有用的归纳偏置。