LDCSF: Local depth convolution-based Swim framework for classifying multi-label histopathology images

TL;DR

This paper introduces LDCSF, a novel deep learning framework combining Swin transformer, local depth convolution, and residual networks to improve multi-label histopathology image classification accuracy for liver cancer diagnosis.

Contribution

The paper proposes the LDCSF model, integrating local depth convolution with Swin transformer and residual networks, to enhance multi-label classification of histopathology images.

Findings

Achieved high classification accuracy: 0.9460 for interstitial area, 0.9960 for necrosis.

Effectively classified non-tumor and tumor regions with accuracy above 0.98.

Provided a foundation for liver cancer microenvironment analysis.

Abstract

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.