Shifting Focus: From Global Semantics to Local Prominent Features in
  Swin-Transformer for Knee Osteoarthritis Severity Assessment
Aymen Sekhri Marouane Tliba Mohamed Amine Kerkouri Yassine Nasser Aladine Chetouani Alessandro Bruno Rachid Jennane
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 https://github.com/mtliba/KOA_NLCS2024