Reinforcement Learning for Automated Anterior Commissure Localization in Brain CT Scans for Tumor Detection

This study explores the efficacy of reinforcement learning (RL) frameworks in analyzing computed tomography (CT) images, aiming to reduce the reliance on labeled data typically required by supervised learning approaches. RL allows the model to make optimal decisions through interactions with the environment, significantly reducing the time needed for CT slice analysis compared to traditional methods. The study also demonstrates the ability to automate the calculation of anterior commissure length, which could help radiologists speed up the diagnostic process by providing accurate measurements for analysis. This research highlights the potential of RL to accelerate medical image analysis, reduce reliance on labeled data, and enhance diagnostic capabilities through the identification of anatomical markers.