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Purpose Supervised deep learning in radiology suffers from notorious inherentlimitations: 1) It requires large, hand-annotated data sets, 2) It isnon-generalizable, and 3) It lacks explainability and intuition. We haverecently proposed Reinforcement Learning to address all threes. However, weapplied it to images with radiologist eye tracking points, which limits thestate-action space. Here we generalize the Deep-Q Learning to a gridworld-basedenvironment, so that only the images and image masks are required.  Materials and Methods We trained a Deep Q network on 30 two-dimensional imageslices from the BraTS brain tumor database. Each image contained one lesion. Wethen tested the trained Deep Q network on a separate set of 30 testing setimages. For comparison, we also trained and tested a keypoint detectionsupervised deep learning network for the same set of training / testing images.  Results Whereas the supervised approach quickly overfit the training data,and predicably performed poorly on the testing set (11\% accuracy), the Deep-Qlearning approach showed progressive improved generalizability to the testingset over training time, reaching 70\% accuracy.  Conclusion We have shown a proof-of-principle application of reinforcementlearning to radiological images, here using 2D contrast-enhanced MRI brainimages with the goal of localizing brain tumors. This represents ageneralization of recent work to a gridworld setting, naturally suitable foranalyzing medical images.

Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets