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

• URL: http://arxiv.org/abs/2308.10446v1
• Date: Mon, 21 Aug 2023 03:44:54 GMT
• Title: LDCSF: Local depth convolution-based Swim framework for classifying multi-label histopathology images
• Authors: Liangrui Pan, Yutao Dou, Zhichao Feng, Liwen Xu, Shaoliang Peng
• Abstract summary: We propose a locally deep convolutional Swim framework (LDCSF) to classify multi-label histopathology images. The classification accuracy of LDCSF for interstitial area, necrosis, non-tumor and tumor reached 0.9460, 0.9960, 0.9808, 0.9847, respectively.
• Score: 4.337832783226794
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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