Integrating Reinforcement Learning to Self Training for Pulmonary Nodule Segmentation in Chest X-rays
This work addresses the labeling burden in medical imaging for clinicians and researchers, but it is incremental as it builds on existing self-training and reinforcement learning methods.
The paper tackled the problem of limited labeled data in medical imaging by integrating reinforcement learning into self-training for pulmonary nodule segmentation in chest X-rays, achieving performance comparable to a standard U-Net with only 50% of labeled data and moderate to significant accuracy improvements when using the same amount of labeled data.
Machine learning applications in medical imaging are frequently limited by the lack of quality labeled data. In this paper, we explore the self training method, a form of semi-supervised learning, to address the labeling burden. By integrating reinforcement learning, we were able to expand the application of self training to complex segmentation networks without any further human annotation. The proposed approach, reinforced self training (ReST), fine tunes a semantic segmentation networks by introducing a policy network that learns to generate pseudolabels. We incorporate an expert demonstration network, based on inverse reinforcement learning, to enhance clinical validity and convergence of the policy network. The model was tested on a pulmonary nodule segmentation task in chest X-rays and achieved the performance of a standard U-Net while using only 50% of the labeled data, by exploiting unlabeled data. When the same number of labeled data was used, a moderate to significant cross validation accuracy improvement was achieved depending on the absolute number of labels used.