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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 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 anno...
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Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder
Lesions that appear hyperintense in both Fluid Attenuated Inversion Reco...
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Image Translation for Medical Image Generation – Ischemic Stroke Lesions
Deep learning-based automated disease detection and segmentation algorit...
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Comparative Analysis of Unsupervised Algorithms for Breast MRI Lesion Segmentation
Accurate segmentation of breast lesions is a crucial step in evaluating ...
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Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation
We present a novel approach of 2D to 3D transfer learning based on mappi...
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AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation
Whole brain segmentation using deep learning (DL) is a very challenging ...
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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 treatment response. We have recently shown that reinforcement learning can be applied to radiological images for lesion localization. Furthermore, we demonstrated that reinforcement learning addresses important limitations of supervised deep learning; namely, it can eliminate the requirement for large amounts of annotated training data and can provide valuable intuition lacking in supervised approaches. However, we did not address the fundamental task of lesion/structure-of-interest segmentation. Here we introduce a method combining unsupervised deep learning clustering with reinforcement learning to segment brain lesions on MRI. Materials and Methods: We initially clustered images using unsupervised deep learning clustering to generate candidate lesion masks for each MRI image. The user then selected the best mask for each of 10 training images. We then trained a reinforcement learning algorithm to select the masks. We tested the corresponding trained deep Q network on a separate testing set of 10 images. For comparison, we also trained and tested a U-net supervised deep learning network on the same set of training/testing images. Results: Whereas the supervised approach quickly overfit the training data and predictably performed poorly on the testing set (16 the unsupervised deep clustering and reinforcement learning achieved an average Dice score of 83 Conclusion: We have demonstrated a proof-of-principle application of unsupervised deep clustering and reinforcement learning to segment brain tumors. The approach represents human-allied AI that requires minimal input from the radiologist without the need for hand-traced annotation.
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