Automated classification of breast cancer subtypes from digital pathology images has been an extremely challenging task due to the complicated spatial patterns of cells in the tissue micro-environment. While newly proposed graph transformers are able to capture more long-range dependencies to enhance accuracy, they largely ignore the topological connectivity between graph nodes, which is nevertheless critical to extract more representative features to address this difficult task. In this paper, we propose a novel connectivity-aware graph transformer (CGT) for phenotyping the topology connectivity of the tissue graph constructed from digital pathology images for breast cancer classification. Our CGT seamlessly integrates connectivity embedding to node feature at every graph transformer layer by using local connectivity aggregation, in order to yield more comprehensive graph representations to distinguish different breast cancer subtypes. In light of the realistic intercellular communication mode, we then encode the spatial distance between two arbitrary nodes as connectivity bias in self-attention calculation, thereby allowing the CGT to distinctively harness the connectivity embedding based on the distance of two nodes. We extensively evaluate the proposed CGT on a large cohort of breast carcinoma digital pathology images stained by Haematoxylin & Eosin. Experimental results demonstrate the effectiveness of our CGT, which outperforms state-of-the-art methods by a large margin. Codes are released on https://github.com/wang-kang-6/CGT.

中文翻译：

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通过连接感知图形转换器对数字病理图像进行乳腺癌分类


由于组织微环境中细胞的空间模式复杂，从数字病理图像中自动分类乳腺癌亚型一直是一项极具挑战性的任务。虽然新提出的图转换器能够捕获更多的远程依赖关系以提高准确性，但它们在很大程度上忽略了图节点之间的拓扑连接性，但这对于提取更具代表性的特征来解决这一艰巨的任务至关重要。在本文中，我们提出了一种新颖的连接感知图转换器（CGT），用于对根据乳腺癌分类的数字病理图像构建的组织图的拓扑连接进行表型分析。我们的 CGT 通过使用局部连接聚合将连接嵌入无缝集成到每个图转换器层的节点特征，以便产生更全面的图表示来区分不同的乳腺癌亚型。根据实际的细胞间通信模式，我们将两个任意节点之间的空间距离编码为自注意力计算中的连接偏差，从而使 CGT 能够根据两个节点的距离独特地利用连接嵌入。我们在大量苏木精和伊红染色的乳腺癌数字病理图像上广泛评估了所提出的 CGT。实验结果证明了我们的 CGT 的有效性，其性能大大优于最先进的方法。代码发布在https://github.com/wang-kang-6/CGT。