Fine-Grained Breast Cancer Classification With Bilinear Convolutional Neural Networks (BCNNs).

Weihuang Liu,Yang Zhang,Mario Juhas

doi:10.3389/fgene.2020.547327

Abstract

Classification of histopathological images of cancer is challenging even for well-trained professionals, due to the fine-grained variability of the disease. Deep Convolutional Neural Networks (CNNs) showed great potential for classification of a number of the highly variable fine-grained objects. In this study, we introduce a Bilinear Convolutional Neural Networks (BCNNs) based deep learning method for fine-grained classification of breast cancer histopathological images. We evaluated our model by comparison with several deep learning algorithms for fine-grained classification. We used bilinear pooling to aggregate a large number of orderless features without taking into consideration the disease location. The experimental results on BreaKHis, a publicly available breast cancer dataset, showed that our method is highly accurate with 99.24% and 95.95% accuracy in binary and in fine-grained classification, respectively.

Highlights

Breast cancer, the most often diagnosed cancer type and the leading cause of cancer-related death among women, was involved in more than 600,000 deaths and 2,000,000 new hospitalizations in 2018 Bray et al (2018)
BreaKHis is a largescale dataset that includes 7909 histopathological images taken from 82 patients, which is divided into two main classes, benign and malignant, and each is further divided into four different subclasses
The fine-grained classification models were trained with 8 types of images with 40 × magnification factors

Summary

Introduction

The most often diagnosed cancer type and the leading cause of cancer-related death among women, was involved in more than 600,000 deaths and 2,000,000 new hospitalizations in 2018 Bray et al (2018). Analysis of histopathological images of breast cancer is still among the main methods of diagnosis. This method suffers from various shortcomings: (1) analysis by inexperienced doctors can lead to wrong diagnosis, (2) overwork may lead to misdiagnosis, and (3) manual diagnosis is both time-consuming and laborious. Novel approaches for automatic diagnosis of the breast cancer with high accuracy and efficiency are urgently needed. With the development of computer vision, automatic cancer image diagnosis has attracted a lot of attention from the scientific community. The development of the slide scanning technology and collection of numerous digital histopathological images enabled computer-based analysis. Previous methods employed hand-crafted features to find a series of hyperplanes in the feature space that formed the optimal decision boundary for the high-dimensional feature space. Doyle et al (2008) calculated more than 3,400 textural and structural features from breast tissue

Methods

Results

Conclusion