Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module

Abstract

Although successful detection of malignant tumors from histopathological images largely depends on the long-term experience of radiologists, experts sometimes disagree with their decisions. Computer-aided diagnosis provides a second option for image diagnosis, which can improve the reliability of experts’ decision-making. Automatic and precision classification for breast cancer histopathological image is of great importance in clinical application for identifying malignant tumors from histopathological images. Advanced convolution neural network technology has achieved great success in natural image classification, and it has been used widely in biomedical image processing. In this paper, we design a novel convolutional neural network, which includes a convolutional layer, small SE-ResNet module, and fully connected layer. We propose a small SE-ResNet module which is an improvement on the combination of residual module and Squeeze-and-Excitation block, and achieves the similar performance with fewer parameters. In addition, we propose a new learning rate scheduler which can get excellent performance without complicatedly fine-tuning the learning rate. We use our model for the automatic classification of breast cancer histology images (BreakHis dataset) into benign and malignant and eight subtypes. The results show that our model achieves the accuracy between 98.87% and 99.34% for the binary classification and achieve the accuracy between 90.66% and 93.81% for the multi-class classification.

Emerging role of deep learning‐based artificial intelligence in tumor pathology

“…[33] developed CNN algorithms to sort each image tile into individual growth pattern and generate a probability map for a WSI, facilitating pathologists to quantitatively report the major and more malignant components of lung adenocarcinoma, such as micropapillary and solid components. Similarly, DL‐based AL were performed for multi‐categorization of colorectal polyp [34], ovarian cancer [35], thyroid tumor [36], breast tumor [37], and cervical squamous cell carcinoma [38]. On the basis of cytological image, AI could recognize the histological subtypes of lung cancer with an accuracy of 60%‐89% [39].…”

A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans

“…Regarding the medical image domain which deals with breast cancer diagnosis, previous studies examined the inclusion of machine learning approaches into breast cancer computer-aided diagnosis and image classification [29,[37][38][39][40][41]. The state of the art on machine learning techniques for breast cancer computer aided diagnosis offers a wide range of analysis regarding the current status of CAD systems, when image modalities used and machine learning-based classifiers are taken into consideration [38,39,42,43].…”

A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival

“…Recent advances in both computational power and convolutional network architectures have greatly increased the applicability of these techniques for several new domains in biology including omics analysis, biomedical signal processing, and biomedical imaging [11]. Specifically, deep learning has been applied to greatly improving detection of regions of interest in BC WSIs [12] and impressive progress has been made in application of deep learning to BC diagnosis from images [13][14][15].…”