Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder
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Abstract (arXiv)
Purpose : Because functional MRI (fMRI) data sets are in general small, we sought a data efficient approach to resting state fMRI classification of autism spectrum disorder (ASD) versus neurotypical (NT) controls. We hypothesized that a Deep Reinforcement Learning (DRL) classifier could learn effectively on a small fMRI training set. Methods : We trained a Deep Reinforcement Learning (DRL) classifier on 100 graph-label pairs from the Autism Brain Imaging Data Exchange (ABIDE) database. For comparison, we trained a Supervised Deep Learning (SDL) classifier on the same training set. Results : DRL significantly outperformed SDL, with a p-value of 2.4 x 10^(-7). DRL achieved superior results for a variety of classifier performance metrics, including an F1 score of 76, versus 67 for SDL. Whereas SDL quickly overfit the training data, DRL learned in a progressive manner that generalised to the separate testing set. Conclusion : DRL can learn to classify ASD versus NT in a data efficient manner, doing so for a small training set. Future work will involve optimizing the neural network for data efficiency and applying the approach to other fMRI data sets, namely for brain cancer patients.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 40 references that are in the map. First posted on arXiv 17 Jun 2022.
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8 of 40 references.
References · 8
- 2021Deep reinforcement learning in medical imaging: A literature reviewcited by 34
- 2020Pooling Regularized Graph Neural Network for fMRI Biomarker Analysiscited by 25
- 2021Deep reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumescited by 12
- 2020Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical imagescited by 7
- 2018Partial Policy-based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Imagescited by 6
- 2020Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training setscited by 5
- 2020Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training setscited by 5
- 2021Deep reinforcement learning-based image classification achieves perfect testing set accuracy for MRI brain tumors with a training set of only 30 imagescited by 4
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