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

doi:10.1007/s12204-024-2705-4

Abstract

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

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

CLC Number:

TP391
R736

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.

References

[1] Global Burden of Disease Cancer Collaboration, FITZMAURICE C, AKINYEMIJU T F, et al. Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 29 cancer groups, 1990. to 2016: A systematic analysis for the global burden of disease study [J]. JAMA Oncology, 2018, 4(11): 1553-1568.
[2] ZHANG Y J, WEI L C, LI J, et al. Status quo and development trend of breast biopsy technology [J]. Gland Surgery, 2013, 2(1): 15-24.
[3] HADJIISKI L, SAHINER B, CHAN H P. Advances in computer-aided diagnosis for breast cancer [J]. Current Opinion in Obstetrics & Gynecology, 2006, 18(1): 64-70.
[4] KAUSHAL C, BHAT S, KOUNDAL D, et al. Recent trends in computer assisted diagnosis (CAD) system for breast cancer diagnosis using histopathological images [J]. IRBM, 2019, 40(4): 211-227.
[5] SHEN D G, WU G R, SUK H I. Deep learning in medical image analysis [J]. Annual Review of Biomedical Engineering, 2017, 19: 221-248.
[6] WANG P, HU X L, LI Y M, et al. Automatic cell nuclei segmentation and classification of breast cancer histopathology images [J]. Signal Processing, 2016, 122(1): 1-13.
[7] XIE J Y, LUTTRELL J IV, ZHANG C Y, et al. Deep learning based analysis of histopathological images of breast cancer [J]. Frontiers in Genetics, 2019, 10: 80.
[8] OSAREH A, SHADGAR B. Machine learning techniques to diagnose breast cancer [C]//2010 5th International Symposium on Health Informatics and Bioinformatics. Ankara: IEEE, 2010: 114-120.
[9] CHEN H L, YANG B, LIU J, et al. A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis [J]. Expert Systems With Applications, 2011, 38(7): 9014-9022.
[10] GUPTA M, GUPTA B. An ensemble model for breast cancer prediction using sequential least squares programming method (SLSQP) [C]//2018 Eleventh International Conference on Contemporary Computing. Noida: IEEE, 2018: 1-3.
[11] KASHIF M, MALIK K R, JABBAR S, et al. Application of machine learning and image processing for detection of breast cancer [M]//Innovation in health informatics. Amsterdam: Elsevier, 2020: 145-162.
[12] SPANHOL F A, OLIVEIRA L S, CAVALIN P R, et al. Deep features for breast cancer histopathological image classification [C]//2017 IEEE International Conference on Systems, Man, and Cybernetics. Banff: IEEE, 2017: 1868-1873.
[13] DAS K, CONJETI S, ROY A G, et al. Multiple instance learning of deep convolutional neural networks for breast histopathology whole slide classification [C]//2018 IEEE 15th International Symposium on Biomedical Imaging. Washington: IEEE, 2018: 578-581.
[14] GUO Y, DONG H H, SONG F Z, et al. Breast cancer histology image classification based on deep neural networks [C]. International conference image analysis and recognition. P′ovoa de Varzim: Springer, 2018: 827-836.
[15] AKBAR S, PEIKARI M, SALAMA S, et al. The transition module: A method for preventing overfitting in convolutional neural networks [J]. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. 2019, 7(3): 260-265.
[16] WEI B Z, HAN Z Y, HE X Y, et al. Deep learning model based breast cancer histopathological image classification [C]//2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis. Chengdu: IEEE, 2017: 348-353.
[17] HE K M, ZHANG X Y, REN S Q, et al. Identity mappings in deep residual networks [M]// Computer vision – ECCV 2016. Amsterdam: Springer, 2016: 630-645.
[18] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition [C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.
[19] HU J, SHEN L, ALBANIE S, et al. Squeeze-andexcitation networks [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011-2023.
[20] LI X, WANG W H, HU X L, et al. Selective kernel networks [C]/ 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 510-519.
[21] KRIZHEVSKY A, SUTSKEVER I, HINTON G E, et al. ImageNet classification with deep convolutional neural networks [J]. Communications of the ACM, 2017, 60(6): 84-90.
[22] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition [C]//3rd International Conference on Learning Representations. San Diego: Computational and Biological Learning Society, 2015: 1-14.
[23] SZEGEDY C, LIU W, JIA Y Q, et al.Going deeper with convolutions [C]//2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015: 1-9.
[24] WANG F, JIANG M Q, QIAN C, et al. Residual attention network for image classification [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 6450-6458.
[25] XU K, BA J L, KIROS R, et al. Show, attend and tell: Neural image caption generation with visual attention [C]//International Conference on Machine Learning. Lille: PMLR, 2015: 2048-2057.
[26] LUCA M, BARLACCHI G, LEPRI B, et al. A survey on deep learning for human mobility [J]. ACM Computing Surveys, 2021, 55(1): 1-44.
[27] LUONG T, PHAM H, MANNING C D. Effective approaches to attention based neural machine translation [C]//2015 Conference on Empirical Methods in Natural Language Processing. Lisbon: Association for Computational Linguistics, 2015: 1412-1421.
[28] HOU Q B, ZHOU D Q, FENG J S. Coordinate attention for efficient mobile network design [C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 13708-13717.
[29] WANG Q L, WU B G, ZHU P F, et al. ECA-net: Efficient channel attention for deep convolutional neural networks [C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 11531-11539.
[30] SIFRE L, MALLAT S. Rigid-motion scattering for texture classification [DB/OL]. (2014-05-07) [2023-05-25]. https:// arxiv.org/abs/1403.1687
[31] M¨uller R, Kornblith S, Hinton G E. When does label smoothing help? [C]//33rd Conference on Neural Information. Vancouver: Curran Associates, 2019: 1-13.
[32] JIANG Y, CHEN L, ZHANG H, et al. Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module [J]. PLoS One, 2019, 14(3): e0214587.
[33] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection [C]//2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 2999-3007.
[34] BARDOU D, ZHANG K, AHMAD S M. Classification of breast cancer based on histology images using convolutional neural networks [J]. IEEE Access, 2018, 6: 24680-24693.
[35] JIANG Y, CHEN L, ZHANG H, et al. Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module [J]. PLoS One, 2019, 14(3): e0214587.
[36] SHEIKH T S, LEE Y, CHO M. Histopathological classification of breast cancer images using a multi-scale input and multi-feature network [J]. Cancers, 2020, 12(8): 2031.
[37] BOUMARAF S, LIU X B, ZHENG Z S, et al. A new transfer learning based approach to magnification dependent and independent classification of breast cancer in histopathological images [J]. Biomedical Signal Processing and Control, 2021, 63: 102192.
[38] HAN Z Y, WEI B Z, ZHENG Y J, et al. Breast cancer multi-classification from histopathological images with structured deep learning model [J]. Scientific Reports, 2017, 7: 4172.
[39] RULANINGTYAS R, HYPERASTUTY A S, RAHAJU A S. Histopathology grading identification of breast cancer based on texture classification using GLCM and neural network method [J]. Journal of Physics: Conference Series, 2018, 1120: 012050.
[40] DIMITROPOULOS K, BARMPOUTIS P, ZIOGA C, et al. Grading of invasive breast carcinoma through grassmannian VLAD encoding [J]. PLoS One, 2017, 12(9): e0185110.
[41] NAHID A A, ALI MEHRABI M, KONG Y N. Histopathological breast cancer image classification by deep neural network techniques guided by local clustering [J]. BioMed Research International, 2018, 2018: 2362108.
[42] LAO Q, FEVENS T. Case-based histopathological malignancy diagnosis using convolutional neural networks [DB/OL]. (2019-05-28) [2023-05-25]. http://arxiv.org/abs/1905.11567
[43] HAMEED Z, ZAHIA S, GARCIA-ZAPIRAIN B, et al. Breast cancer histopathology image classification using an ensemble of deep learning models [J]. Sensors, 2020,20(16): 4373.