Enabling Autonomous Medical Image Data Annotation: A human-in-the-loop Reinforcement Learning Approach

Abstract

Deep learning techniques have shown significant contributions to several fields, including medical image analysis. For supervised learning tasks, the performance of these techniques depends on a large amount of training data as well as labeled data. However, labeling is an expensive and time-consuming process. With this limitation, we introduce a new approach based on Deep Reinforcement Learning (DRL) to cost-effective annotation in a set of medical data. Our approach consists of a virtual agent to automatically label training data, and a human-in-the-loop to assist in the training of the agent. We implemented the Deep Q-Network algorithm to create the virtual agent and adopted the method mentioned above, which employs human advice to the virtual agent. Our approach was evaluated on a set of medical X-ray data in different use cases, where the agent was required to create new annotations in the form of bounding boxes from unlabeled data. Results show that an agent training with advice positively impacts obtaining new annotations from a data set with scarce labels. This result opens up new possibilities for advancing the study and implementing autonomous approaches with human advice to create a cost-effective annotation in data sets for computer-aided medical image analysis.