Adaptive Curriculum Query Strategy for Active Learning in Medical Image Classification

Deep active learning (AL) is commonly used to reduce labeling costs in medical image analysis. Deep learning (DL) models typically exhibit a preference for learning from easy data and simple patterns before they learn from complex ones. However, existing AL methods often employ a fixed query strategy for sample selection, which may cause the model to focus too closely on challenging-to-classify data. The result is a deceleration of the convergence of DL models and an increase in the amount of labeled data required to train them. To address this issue, we propose a novel Adaptive Curriculum Query Strategy for AL in Medical Image Classification. During the training phase, our strategy leverages Curriculum Learning principles to initially prioritize the selection of a diverse range of samples to cover various difficulty levels, facilitating rapid model convergence. Once the distribution of the selected samples closely matches that of the entire dataset, the query strategy shifts its focus towards difficult-to-classify data based on uncertainty. This novel approach enables the model to achieve superior performance with fewer labeled samples. We perform extensive experiments demonstrating that our model not only requires fewer labeled samples but outperforms state-of-the-art models in terms of efficiency and effectiveness. The code is publicly available at https://github.com/HelenMa9998/Easy_hard_AL .