Label-Efficient Breast Cancer Histopathological Image Classification

作     者：Qi, Qi Li, Yanlong Wang, Jitian Zheng, Han Huang, Yue Ding, Xinghao Rohde, Gustavo Kunde 

作者机构：Xiamen Univ Sch Informat Sci & Engn Fujian Key Lab Sensing & Comp Smart City Xiamen 361005 Fujian Peoples R China Univ Virginia Dept Biomed Engn Charlottesville VA 22908 USA Univ Virginia Dept Elect & Comp Engn Charlottesville VA 22908 USA 

出 版 物：《IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS》 (IEEE J. Biomedical Health Informat.)

年 卷 期：2019年第23卷第5期

页      面：2108-2116页

核心收录：

学科分类：0710[理学-生物学] 0808[工学-电气工程] 1001[医学-基础医学(可授医学、理学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [81671766, 61571382, 61571005, 61172179, 61103121] Fundamental Research Funds for the Central Universities [20720160075, 20720180059] CCF-Tencent open fund National Natural Science Foundation of Fujian Province, China [2017J01126] 

主　　题：Breast cancer histopathological image classification deep learning active learning query strategy 

摘      要：The automatic classification of breast cancer histopathological images has great significance in computer-aided diagnosis. Recently, deep learning via neural networks has enabled pattern detection and prediction using large, labeled datasets;whereas, collecting and annotating sufficient histological data using professional pathologists is time consuming, tedious, and extremely expensive. In the proposed paper, a deep active learning framework is designed and implemented for classification of breast cancer histopathological images, with the goal of maximizing the learning accuracy from very limited labeling. This method involves manual annotation of the most valuable unlabeled samples, which are then integrated into the training set. The model is then iteratively updated with an increasing training set. Here, two selection strategies are discussed for the proposed deep active learning framework: An entropy-based strategy and a confidence-boosting strategy. The proposed method has been validated using a publicly available breast cancer histopathological image dataset, wherein each image patch is binarily classified as benign or malignant. The experimental results demonstrate that, compared with a random selection, our proposed framework can reduce annotation costs up to 66.67%, with higher accuracy and less expensive annotation than standard query strategy.