HAL: Hybrid active learning for efficient labeling in medical domain

Abstract The success of the deep convolutional neural networks in computer vision tasks mainly relies on massive labeled training data. However, in the field of medical images, it is difficult to construct large labeled datasets since the labeling of medical images is time-consuming, labor-intensive, and medical expertise demanded. To meet the challenge, we propose a hybrid active learning framework HAL for efficient labeling in the medical domain, which integrates active learning into deep learning to reduce the cost of manual labeling and take the advantages of deep neural networks. The proposed HAL utilizes a hybrid sampling strategy considering both sample diversity and prediction loss simultaneously. The effectiveness and efficiency of proposed HAL are validated on three medical image datasets. The experimental results show that the proposed HAL outperforms several state-of-the-art active learning methods. On the Hyper-Kvasir Dataset, with only 10% of the labels, the HAL achieves 95% performance of the deep learning method trained on the entire dataset. The quantitative and qualitative analysis proves that HAL can greatly reduce the number of labels needed for training a deep neural network, which is robust to address efficient labeling problems even with imbalanced data distribution.