用于全切片图像诊断的语义感知注意力引导
Semantics-Aware Attention Guidance for Diagnosing Whole Slide Images
AI总结：
针对全切片图像尺寸大、空间关系复杂导致传统MIL癌症诊断准确率不足的问题，提出包含注意力信号转换技术与灵活注意力损失的SAG框架，在两个癌症数据集上提升了诊断性能，且可适配任意基于注意力的诊断模型。
摘要：
准确的癌症诊断仍是数字病理学领域的一项关键挑战，这很大程度上源于全切片图像的十亿像素级尺寸以及其中复杂的空间关系。传统的多示例学习（MIL）方法往往难以应对这些复杂问题，尤其是在保留准确诊断所需的上下文信息方面。为此，我们提出了一种名为Semantics-Aware Attention Guidance（SAG，语义感知注意力引导）的新型框架，该框架包含两部分：1）一种将诊断相关实体转化为注意力信号的技术；2）一种灵活的注意力损失函数，可有效整合各类具有语义重要性的信息，例如组织解剖结构和癌变区域。我们在两个不同的癌症数据集上开展的实验表明，搭配两种当前最优的基线模型时，该方法在准确率、精确率和召回率上均实现了稳定提升。定性分析进一步显示，启发式引导的引入使模型能够聚焦于诊断的关键区域。SAG不仅对本文讨论的模型有效，其适配性还可扩展至任何基于注意力的诊断模型。这为进一步提升癌症诊断的准确率和效率开辟了令人期待的可能性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
