Breast Cancer Histopathological Image Classification via Deep Active Learning and Confidence Boosting

Classify image into benign and malignant is one of the basic image processing tools in digital pathology for breast cancer diagnosis. Deep learning methods have received more attention recently by training with large-scale labeled datas, but collecting and annotating clinical data is professional and time-consuming. The proposed work develops a deep active learning framework to reduce the annotation burden, where the method actively selects the valuable unlabeled samples to be annotated instead of random selecting. Besides, compared with standard query strategy in previous active learning methods, the proposed query strategy takes advantage of manual labeling and auto-labeling to emphasize the confidence boosting effect. We validate the proposed work on a public histopathological image dataset. The experimental results demonstrate that the proposed method is able to reduce up to 52% labeled data compared with random selection. It also outperforms deep active learning method with standard query strategy in the same tasks.