2018/04/18 by Xinpeng Xie, Yuexiang Li, Xie, Xinpeng +3
Biochemistry, Genetics and Molecular Biology · Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Gene expression and cancer classification #Image Retrieval and Classification Techniques #Machine Learning and Algorithms
paper · pdf · doi:10.48550/arxiv.1804.06670
openalex publication_date 2018/04/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Breast cancer is the second most common malignancy among women and has become a major public health problem in current society. Traditional breast cancer identification requires experienced pathologists to carefully read the breast slice, which is laborious and suffers from inter-observer variations. Consequently, an automatic classification framework for breast cancer identification is worthwhile to develop. Recent years witnessed the development of deep learning technique. Increasing number of medical applications start to use deep learning to improve diagnosis accuracy. In this paper, we proposed a novel training strategy, namely reversed active learning (RAL), to train network to automatically classify breast cancer images. Our RAL is applied to the training set of a simple convolutional neural network (CNN) to remove mislabeled images. We evaluate the CNN trained with RAL on publicly available ICIAR 2018 Breast Cancer Dataset (IBCD). The experimental results show that our RAL increases the slice-based accuracy of CNN from 93.75% to 96.25%.