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.

精确的癌症诊断在数字病理学中仍然是一个重要挑战，主要是由于全幻灯片图像中的巨像素大小和复杂空间关系所致。本文引入一种名为 Semantics-Aware Attention Guidance（SAG）的新框架，通过将诊断相关实体转换为注意信号和灵活的注意力损失，有效地整合了组织解剖和癌症区域等语义信息，在两个不同的癌症数据集上的实验证明了模型准确性、精度和召回率的持续改进，并揭示了启发式引导的结合使模型能够关注诊断关键区域。SAG 不仅对于所讨论的模型有效，其适应性还可扩展到任何基于注意力的诊断模型，从而为进一步提高癌症诊断的准确性和效率打开了令人兴奋的可能性。