图像分析在支持肺癌的组织病理学诊断方面可以发挥重要作用，深度学习方法已经取得了显着的成果。然而，由于全切片图像 (WSI) 规模较大，由病理学家专家创建手动像素注释既昂贵又耗时。此外，肿瘤的异质性和肿瘤亚型形态表型的相似性导致观察者之间注释的差异，这限制了最佳性能。有效使用弱标签可能会缓解这些问题。在本文中，我们提出了一种基于两阶段 Transformer 的弱监督学习框架，称为 Simple Shuffle-Remix Vision Transformer (SSRViT)。首先，我们引入了 Shuffle-Remix Vision Transformer (SRViT) 来检索有判别性的局部标记并提取有效的代表性特征。然后，选择并聚合令牌特征以生成 WSI 的稀疏表示，并将其输入到简单的基于转换器的分类器 (SViT) 中以进行滑动级预测。实验结果表明，与其他最先进的方法相比，我们提出的 SSRViT 在区分腺癌、肺硬化性肺细胞瘤和正常肺组织方面的性能显着提高（准确度为 96.9%，AUC 为 99.6%）。

Image analysis can play an important role in supporting histopathological diagnoses of lung cancer, with deep learning methods already achieving remarkable results. However, due to the large scale of whole-slide images (WSIs), creating manual pixel-wise annotations from expert pathologists is expensive and time-consuming. In addition, the heterogeneity of tumors and similarities in the morphological phenotype of tumor subtypes have caused inter-observer variability in annotations, which limits optimal performance. Effective use of weak labels could potentially alleviate these issues. In this paper, we propose a two-stage transformer-based weakly supervised learning framework called Simple Shuffle-Remix Vision Transformer (SSRViT). Firstly, we introduce a Shuffle-Remix Vision Transformer (SRViT) to retrieve discriminative local tokens and extract effective representative features. Then, the token features are selected and aggregated to generate sparse representations of WSIs, which are fed into a simple transformer-based classifier (SViT) for slide-level prediction. Experimental results demonstrate that the performance of our proposed SSRViT is significantly improved compared with other state-of-the-art methods in discriminating between adenocarcinoma, pulmonary sclerosing pneumocytoma and normal lung tissue (accuracy of 96.9% and AUC of 99.6%).