Histopathological images are the gold standard for diagnosing liver cancer. However, the accuracy of fully digital diagnosis in computational pathology needs to be improved. In this paper, in order to solve the problem of multi-label and low classification accuracy of histopathology images, we propose a locally deep convolutional Swim framework (LDCSF) to classify multi-label histopathology images. In order to be able to provide local field of view diagnostic results, we propose the LDCSF model, which consists of a Swin transformer module, a local depth convolution (LDC) module, a feature reconstruction (FR) module, and a ResNet module. The Swin transformer module reduces the amount of computation generated by the attention mechanism by limiting the attention to each window. The LDC then reconstructs the attention map and performs convolution operations in multiple channels, passing the resulting feature map to the next layer. The FR module uses the corresponding weight coefficient vectors obtained from the channels to dot product with the original feature map vector matrix to generate representative feature maps. Finally, the residual network undertakes the final classification task. As a result, the classification accuracy of LDCSF for interstitial area, necrosis, non-tumor and tumor reached 0.9460, 0.9960, 0.9808, 0.9847, respectively. Finally, we use the results of multi-label pathological image classification to calculate the tumor-to-stromal ratio, which lays the foundation for the analysis of the microenvironment of liver cancer histopathological images. Second, we released a multilabel histopathology image of liver cancer, our code and data are available at https://github.com/panliangrui/LSF.

翻译：组织病理学图像是诊断肝癌的金标准。然而，计算病理学中全数字诊断的准确性仍需提升。针对组织病理学图像多标签及低分类准确率的问题，本文提出局部深度卷积Swim框架（LDCSF）用于多标签组织病理学图像分类。为提供局部视野诊断结果，我们构建了包含Swin Transformer模块、局部深度卷积（LDC）模块、特征重构（FR）模块及ResNet模块的LDCSF模型。Swin Transformer模块通过将注意力机制限制在每个窗口内来减少计算量。随后，LDC模块重构注意力图并在多通道执行卷积操作，将生成的特征图传递至下一层。FR模块利用各通道获得的权重系数向量与原始特征图向量矩阵进行点积运算，生成代表性特征图。最终，残差网络承担最终分类任务。实验结果表明，LDCSF对间质区域、坏死区域、非肿瘤及肿瘤组织的分类准确率分别达到0.9460、0.9960、0.9808、0.9847。最后，我们利用多标签病理图像分类结果计算肿瘤-间质比，为肝癌组织病理学图像微环境分析奠定基础。同时，我们发布了肝癌多标签组织病理学图像数据集，相关代码与数据开源地址为https://github.com/panliangrui/LSF。