A Reinforcement Learning Framework for Medical Image Segmentation

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Reinforcement Learning in Healthcare: A Survey (2019, methodology): “ta pertains to various processing and analysis tasks in medical image examination, such as feature extracting, image segmentation, and object detection/localization/tracing [248], [249]. Sahba et al. [250], [251], [252], [253] applied basic Q-learning to the segmentation of the prostate in transrect”
Evaluating Reinforcement Learning Agents for Anatomical Landmark Detection (2019, unknown): “Because of the recent success of employing Q-learning in several medical imaging applications [5, 9, 25, 26, 28, 29, 30, 31], we adopt in this paper the common strategy of Q-learning based methods as a solution for the RL problem formulation of landmark detection.”
Machine Learning and Radiology (2012, background): “have shown high potential of applying reinforcement learning in medical image segmentation (Sahba et al., 2006).”
Application on Reinforcement Learning for Diagnosis Based on Medical Image (2008, methodology): “(Sahba et al, 2006) introduced a new method for medical image segmentation using a reinforcement learning scheme.”
Application of Opposition-Based Reinforcement Learning in Image Segmentation (2007, background): “The design of RL agents is based on the definition of the problem at hand.”