Shifting Focus: From Global Semantics to Local Prominent Features in Swin-Transformer for Knee Osteoarthritis Severity Assessment

Imaging diagnostics frequently bottleneck on manual inspection, which introduces delays and inconsistency. Deep learning offers a path to automation, but foundation models in computer vision tend to emphasise global context at the expense of the local structures clinicians actually rely on.

We keep the Swin Transformer’s capacity to capture long-range spatial dependencies while refining the representation of clinically salient regions. A localisation-aware refinement module aligns hierarchical transformer tokens with the final classifier distribution, so local cues stay discriminative across every scale. Validation on two public benchmarks shows improved robustness and precision for Kellgren–Lawrence severity prediction.