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

TL;DR

This study demonstrates that Deep Q Networks, a reinforcement learning approach, can accurately localize brain tumors in MRI images with very small training sets, outperforming supervised methods that tend to overfit.

Contribution

The paper introduces a generalized Deep Q learning method in a gridworld environment for medical image analysis, reducing the need for extensive annotations.

Findings

Deep Q learning achieved 70% accuracy on test images.

Supervised keypoint detection overfitted with only 11% accuracy.

Reinforcement learning showed improved generalization over training.

Abstract

Purpose Supervised deep learning in radiology suffers from notorious inherent limitations: 1) It requires large, hand-annotated data sets, 2) It is non-generalizable, and 3) It lacks explainability and intuition. We have recently proposed Reinforcement Learning to address all threes. However, we applied it to images with radiologist eye tracking points, which limits the state-action space. Here we generalize the Deep-Q Learning to a gridworld-based environment, so that only the images and image masks are required. Materials and Methods We trained a Deep Q network on 30 two-dimensional image slices from the BraTS brain tumor database. Each image contained one lesion. We then tested the trained Deep Q network on a separate set of 30 testing set images. For comparison, we also trained and tested a keypoint detection supervised 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-Q learning approach showed progressive improved generalizability to the testing set over training time, reaching 70\% accuracy. Conclusion We have shown a proof-of-principle application of reinforcement learning to radiological images, here using 2D contrast-enhanced MRI brain images with the goal of localizing brain tumors. This represents a generalization of recent work to a gridworld setting, naturally suitable for analyzing medical images.