基于多注意力卷积神经网络的乳腺癌组织学图像诊断

doi:10.1007/s12204-024-2705-4

摘要/Abstract

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习多分类框架，引入多注意机制和选择核卷积替换普通卷积，同时对不同注意力分配权重和组合，在模型的精度指标和速率上有提升。此外，本文还实现对学习速率调度器作出对比，使用误差函数能够在微调学习速率实现良好的性能，在标签中通过使用标签软化来减少模型错误识别所造成的损失误差，在损失函数中赋予不同的类别权重来平衡正负样本不平衡问题。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%~99.50%，多分类准确率在97.89%与98.11%之间。

关键词: 乳腺癌，深度学习，注意力机制，分类诊断

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 99.50%, and that of multipl classifications ranged from 97.89% to 98.11%.

Key words: breast cancer, deep learning, attentional mechanism, classification diagnosis factorization

中图分类号:

TP391
R736

徐旺旺1,2，许良凤1,2，刘宁徽3，律娜3. 基于多注意力卷积神经网络的乳腺癌组织学图像诊断[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 91-106.

XU Wangwang^1，2 (徐旺旺), XU Liangfeng^1，2 (许良凤), LIU Ninghui³(刘宁徽), LU Na³(律娜). Histological Image Diagnosis of Breast Cancer Based on Multi-Attention Convolution Neural Network[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 91-106.

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