DBrAL: A Novel Uncertainty-Based Active Learning Based on Deep-Broad Learning for Medical Image Classification

Abstract

Medical image classification is the foundational task in medical image analysis which depends on the high-quality annotated training data. Due to the high cost of medical image annotation and the strong heterogeneity of medical data, how to acquire high-quality annotation under the limited labeling budget has become a core requirement. Active learning (AL) provides a human-in-the-loop labeling scheme to reduce the labeling cost by selecting more valuable samples from unlabeled sets to be labeled. Uncertainty-based and diversity-based methods are the most mainstream active learning strategy. Recently, gradient information has been innovatively used to estimate model change as uncertainty and diversity for data sampling. However, gradient-based approaches may cause the problem of gradient vanishing or exploding by insufficient training samples. To avoid this problem, our idea is to measure the model change of each query sample in a gradient-free way. This paper proposes a deep-broad active learning (DBrAL) strategy to achieve this by utilizing the incremental learning of a broad learning system (BLS) to calculate the weight changes. DBrAL first extracts the features from the deep learner and uses them to fix the BLS. Then, incremental learning is used to quantifyă the weight change for each query sample. Since DBrAL directly establishes the relationship between query samples and the classification hyperplane, it avoids the problem of gradients, which can be more reliable. In the experiment, we demonstrate the effectiveness of our DBrAL on two public medical image classification datasets compared with several SOTA methods.