Semantics-Aware Attention Guidance for Diagnosing Whole Slide Images
Kechun Liu Wenjun Wu Joann G. Elmore Linda G. Shapiro
Accurate cancer diagnosis remains a critical challenge in digital pathology,
largely due to the gigapixel size and complex spatial relationships present in
whole slide images. Traditional multiple instance learning (MIL) methods often
struggle with these intricacies, especially in preserving the necessary context
for accurate diagnosis. In response, we introduce a novel framework named
Semantics-Aware Attention Guidance (SAG), which includes 1) a technique for
converting diagnostically relevant entities into attention signals, and 2) a
flexible attention loss that efficiently integrates various semantically
significant information, such as tissue anatomy and cancerous regions. Our
experiments on two distinct cancer datasets demonstrate consistent improvements
in accuracy, precision, and recall with two state-of-the-art baseline models.
Qualitative analysis further reveals that the incorporation of heuristic
guidance enables the model to focus on regions critical for diagnosis. SAG is
not only effective for the models discussed here, but its adaptability extends
to any attention-based diagnostic model. This opens up exciting possibilities
for further improving the accuracy and efficiency of cancer diagnostics.