Conventional imaging diagnostics frequently encounter bottlenecks due to
manual inspection, which can lead to delays and inconsistencies. Although deep
learning offers a pathway to automation and enhanced accuracy, foundational
models in computer vision often emphasize global context at the expense of
local details, which are vital for medical imaging diagnostics. To address
this, we harness the Swin Transformer's capacity to discern extended spatial
dependencies within images through the hierarchical framework. Our novel
contribution lies in refining local feature representations, orienting them
specifically toward the final distribution of the classifier. This method
ensures that local features are not only preserved but are also enriched with
task-specific information, enhancing their relevance and detail at every
hierarchical level. By implementing this strategy, our model demonstrates
significant robustness and precision, as evidenced by extensive validation of
two established benchmarks for Knee OsteoArthritis (KOA) grade classification.
These results highlight our approach's effectiveness and its promising
implications for the future of medical imaging diagnostics. Our implementation
is available on this https URL

通过使用 Swin Transformer 以及局部特征表示的改进方法，本研究在骨关节炎等医学影像诊断领域实现了显著的鲁棒性和精度提升。