This paper explores the problem of breast tissue classification of microscopy images. Based on the predominant cancer type the goal is to classify images into four categories of normal, benign, in situ carcinoma, and invasive carcinoma. Given a suitable training dataset, we utilize deep learning techniques to address the classification problem. Due to the large size of each image in the training dataset, we propose a patch-based technique which consists of two consecutive convolutional neural networks. The first "patch-wise" network extracts features of image patches while the second "image-wise" network performs classification of the whole image. The first network is aimed at extracting local information while the second network obtains global information of an input image. We trained the networks using the ICIAR 2018 grand challenge on BreAst Cancer Histology (BACH) dataset. The proposed method yields 95% accuracy on the validation set compared to previously reported 77% accuracy rates in the literature. Our code is publicly available at https://github.com/ImagingLab/ICIAR2018

此论文研究了乳腺组织的分类问题，提出了一种基于卷积神经网络的“补丁-图像”分类方法，并利用ICIAR2018数据集进行验证，在验证数据集上获得了95%的分类精度。

乳腺癌组织学图像分类的两级卷积神经网络