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

作　　者：XU Wangwang XU Liangfeng LIU Ninghui LU Na 徐旺旺;许良凤;刘宁徽;律娜(Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology),Ministry of Education,Hefei,230601,China;School of Computer and Information,Hefei University of Technology,Hefei,230601,China;First Affiliated Hospital of Anhui Medical University,Hefei,230022,China)

机构地区：[1]Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology),Ministry of Education,Hefei,230601,China [2]School of Computer and Information,Hefei University of Technology,Hefei,230601,China [3]First Affiliated Hospital of Anhui Medical University,Hefei,230022,China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2025年第1期91-106,共16页上海交通大学学报(英文版)

基　　金：the University Synergy Innovation Program of Anhui Province(No.GXXT-2022-041)。

摘　　要：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%.

关 键 词：breast cancer deep learning attentional mechanism classification diagnosis 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] R736[自动化与计算机技术—计算机科学与技术]