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Histological Image Diagnosis of Breast Cancer Based on Multi-Attention Convolution Neural Network

基于多注意力卷积神经网络的乳腺癌组织学图像诊断

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Abstract

Breast cancer is a serious and high morbidity disease in women, and it is the main cause of cancer death in China. However, getting tested and diagnosed early can reduce the risk of cancer. At present, there are clinical examinations, imaging screening and biopsies, among which histopathological examination is the gold standard. However, the process is complicated and time-consuming, and misdiagnosis may exist. This paper puts forward a classification framework based on deep learning, introducing multi-attention mechanism, selecting kernel convolution instead of ordinary convolution, and using different weights and combinations to pay attention to the accuracy index and growth rate of the model. In addition, we also compared the learning rate regulators. Error function can fine-tune the learning rate to achieve good performance, using label softening to reduce the loss error caused by model error recognition in the label, and assigning different category weights in the loss function to balance the positive and negative samples. We used the BreakHis data set to automatically classify histological images into benign and malignant, four categories and eight subtypes. Experimental results showed that the accuracy of binary classifications ranged from 98.23% to 98.83%, and that of multiple classifications ranged from 97.89% to 98.11%.

摘要

乳腺癌是在女性中致病严重并且发病率较高的疾病, 是全国女性癌症死亡的主要原因。然而, 提前进行检查和诊断可以减少癌症的风险。现在, 对于乳腺癌的诊断方法有临床检查、影像学筛查和活组织检查, 其中组织病理学检查是金标准。但整个过程比较复杂和耗时, 可能还会存在误诊情况。本文提出利用深度学**衡**负样本不**衡问题。我们采用BreakHis数据集自动分类乳腺癌组织学图像为良性和恶性、四分类以及八个亚型。实验结果表明, 二分类准确率为98.23%∼98.83%, 多分类准确率在97.89%与98.11%之间。

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Acknowledgments

This work is an innovative study based on the breast cancer histopathology data set BreakHis provided by P&D Laboratories.

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Authors and Affiliations

  1. Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), Ministry of Education, Hefei, 230601, China

    Wangwang Xu  (徐旺旺) & Liangfeng Xu  (许良凤)

  2. School of Computer and Information, Hefei University of Technology, Hefei, 230601, China

    Wangwang Xu  (徐旺旺) & Liangfeng Xu  (许良凤)

  3. First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China

    Ninghui Liu  (刘宁徽) & Na Lü  (律娜)

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  1. Wangwang Xu  (徐旺旺)
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  2. Liangfeng Xu  (许良凤)
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  3. Ninghui Liu  (刘宁徽)
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  4. Na Lü  (律娜)
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Correspondence to Na Lü  (律娜).

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Conflict of Interest The authors declare that they have no potential conflict of interest.

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Foundation item: the University Synergy Innovation Program of Anhui Province (No. GXXT-2022-041)

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Xu, W., Xu, L., Liu, N. et al. Histological Image Diagnosis of Breast Cancer Based on Multi-Attention Convolution Neural Network. J. Shanghai Jiaotong Univ. (Sci.) (2024). https://doi.org/10.1007/s12204-024-2705-4

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  • DOI: https://doi.org/10.1007/s12204-024-2705-4

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