期刊
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
卷 23, 期 5, 页码 2108-2116出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2018.2885134
关键词
Breast cancer; histopathological image classification; deep learning; active learning; query strategy
类别
资金
- 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]
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.
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