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 this https URL

为了提高组织病理图像的多标签分类准确性，本文提出了一种局部深度卷积浮游 (Swin) 框架 (LDCSF) 来分类多标签组织病理图像，并基于该分类结果计算了肝癌组织病理图像的肿瘤至基质比率。