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Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets
Purpose Supervised deep learning in radiology suffers from notorious inh...
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Deep reinforcement learning-based image classification achieves perfect testing set accuracy for MRI brain tumors with a training set of only 30 images
Purpose: Image classification may be the fundamental task in imaging art...
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Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training sets
Purpose: Lesion segmentation in medical imaging is key to evaluating tre...
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Brain Tumor Classification Using Deep Learning Technique – A Comparison between Cropped, Uncropped, and Segmented Lesion Images with Different Sizes
Deep Learning is the newest and the current trend of the machine learnin...
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Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots
Exploration in an unknown environment is the core functionality for mobi...
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MRI Images, Brain Lesions and Deep Learning
Medical brain image analysis is a necessary step in Computer Assisted /A...
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EEG-based Drowsiness Estimation for Driving Safety using Deep Q-Learning
Fatigue is the most vital factor of road fatalities and one manifestatio...
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Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images
Purpose: AI in radiology is hindered chiefly by: 1) Requiring large annotated data sets. 2) Non-generalizability that limits deployment to new scanners / institutions. And 3) Inadequate explainability and interpretability. We believe that reinforcement learning can address all three shortcomings, with robust and intuitive algorithms trainable on small datasets. To the best of our knowledge, reinforcement learning has not been directly applied to computer vision tasks for radiological images. In this proof-of-principle work, we train a deep reinforcement learning network to predict brain tumor location. Materials and Methods: Using the BraTS brain tumor imaging database, we trained a deep Q network on 70 post-contrast T1-weighted 2D image slices. We did so in concert with image exploration, with rewards and punishments designed to localize lesions. To compare with supervised deep learning, we trained a keypoint detection convolutional neural network on the same 70 images. We applied both approaches to a separate 30 image testing set. Results: Reinforcement learning predictions consistently improved during training, whereas those of supervised deep learning quickly diverged. Reinforcement learning predicted testing set lesion locations with 85 accuracy, compared to roughly 7 Conclusion: Reinforcement learning predicted lesions with high accuracy, which is unprecedented for such a small training set. We believe that reinforcement learning can propel radiology AI well past the inherent limitations of supervised deep learning, with more clinician-driven research and finally toward true clinical applicability.
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