STAMP：用于多中心组织病理学图像STAS诊断的多模式注意力感知多示例学习
STAMP: Multi-pattern Attention-aware Multiple Instance Learning for STAS Diagnosis in Multi-center Histopathology Images
AI总结：
针对肺腺癌气腔内播散（STAS）诊断难题，提出多模式注意力感知多示例学习框架STAMP，利用双分支架构与相似性正则化提取病理特征，在多中心数据集上取得超越临床水平的诊断效果。
摘要：
气腔内播散（STAS）构成了肺腺癌（LUAD）的一种新型侵袭模式，与肿瘤复发和生存率降低相关。然而，LUAD的大规模STAS诊断是一项劳动密集型工作，加上其独特的病理特征和形态特点，容易导致漏诊和误诊。因此，临床上迫切需要利用深度学习模型进行STAS诊断。本研究最初从中南大学湘雅二医院和湘雅三医院的STAS患者以及TCGA-LUAD队列中收集了组织病理学图像。三位资深病理学家进行了交叉验证标注，构建了STAS-SXY、STAS-TXY和STAS-TCGA数据集。随后，我们提出了一种名为STAMP的多模式注意力感知多示例学习框架，用于分析和诊断多中心组织病理学图像中STAS的存在。具体而言，双分支架构引导模型从不同的语义空间学习与STAS相关的病理特征。基于Transformer的实例编码和多模式注意力聚合模块动态选择与STAS病理密切相关的区域，抑制无关噪声并增强全局表示的判别能力。此外，相似性正则化约束防止了跨分支的特征冗余，从而提高了整体诊断准确性。大量实验表明，STAMP在STAS-SXY、STAS-TXY和STAS-TCGA上取得了具有竞争力的诊断结果，AUC分别为0.8058、0.8017和0.7928，超越了临床水平。
英文摘要：
Spread through air spaces (STAS) constitutes a novel invasive pattern in lung adenocarcinoma (LUAD), associated with tumor recurrence and diminished survival rates. However, large-scale STAS diagnosis in LUAD remains a labor-intensive endeavor, compounded by the propensity for oversight and misdiagnosis due to its distinctive pathological characteristics and morphological features. Consequently, there is a pressing clinical imperative to leverage deep learning models for STAS diagnosis. This study initially assembled histopathological images from STAS patients at the Second Xiangya Hospital and the Third Xiangya Hospital of Central South University, alongside the TCGA-LUAD cohort. Three senior pathologists conducted cross-verification annotations to construct the STAS-SXY, STAS-TXY, and STAS-TCGA datasets. We then propose a multi-pattern attention-aware multiple instance learning framework, named STAMP, to analyze and diagnose the presence of STAS across multi-center histopathology images. Specifically, the dual-branch architecture guides the model to learn STAS-associated pathological features from distinct semantic spaces. Transformer-based instance encoding and a multi-pattern attention aggregation modules dynamically selects regions closely associated with STAS pathology, suppressing irrelevant noise and enhancing the discriminative power of global representations. Moreover, a similarity regularization constraint prevents feature redundancy across branches, thereby improving overall diagnostic accuracy. Extensive experiments demonstrated that STAMP achieved competitive diagnostic results on STAS-SXY, STAS-TXY and STAS-TCGA, with AUCs of 0.8058, 0.8017, and 0.7928, respectively, surpassing the clinical level.
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。
