Adaptive-Masking Policy with Deep Reinforcement Learning for Self-Supervised Medical Image Segmentation

Although self-supervised learning methods based on masked image modeling have achieved some success in improving the performance of deep learning models, these methods have difficulty in ensuring that the masked region is the most appropriate for each image, resulting in segmentation networks that do not get the best weights in pre-training. Therefore, we propose a new adaptive-masking policy self-supervised learning method. Specifically, we model the process of masking images as a reinforcement learning problem and use the results of the reconstruction model as a feedback signal to guide the agent to learn the masking policy to select a more appropriate mask position and size for each image, helping the reconstruction network to learn more fine-grained image representation information and thus improve the downstream segmentation model performance. We conduct extensive experiments on two datasets, Cardiac and TCIA, and the results show that our approach outperforms current state-of-the-art self-supervised learning methods.