期刊
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
卷 63, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2020.102192
关键词
Breast cancer; Histopathology; BreaKHis; CNN; ResNet; Transfer learning; Block-wise fine-tuning
资金
- Beijing Municipal Science and Technology Project [Z181100001918002]
This paper proposes a new transfer learning-based approach to automated classification of breast cancer from histopathological images using ResNet-18. The method refines the deep neural network on histopathological images through block-wise fine-tuning strategy, achieving promising results on the BreaKHis dataset.
The visual analysis of histopathological images is the gold standard for diagnosing breast cancer, yet a strenuous and an intricate task that requires years of pathologist training. Therefore, automating this task using computer-aided diagnosis (CAD) is highly expected. This paper proposes a new transfer learning-based approach to automated classification of breast cancer from histopathological images, including magnification dependent (MD) and magnification independent (MI) binary and eight-class classifications. We apply the deep neural network ResNet-18 to this problem, which is pre-trained on ImageNet, a large dataset of common images. We then design our transfer learning method to refine the network on histopathological images. Our transfer learning method is based on block-wise fine-tuning strategy; in which we make the last two residual blocks of the deep network model more domain-specific to our target data. It substantially helps to avoid over-fitting and speed up the training. Furthermore, we strengthen the adaptability of the proposed approach by using global contrast normalization (GCN) based on the target's data values and three-fold data augmentation on training data. The experimental results of MD and MI binary and eight-class classifications on the publicly available BreaKHis dataset demonstrate that our approach is promising and effective, outperforming recent state-of-the-art MD and MI counterparts by a fair margin.
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