Search Results for author:
Found 11 papers, 0 papers with code
Kinematics Modeling of Peroxy Free Radicals: A Deep Reinforcement Learning Approach
Tropospheric ozone, known as a concerning air pollutant, has been associated with health issues including asthma, bronchitis, and impaired lung function.
Deep neuroevolution to predict primary brain tumor grade from functional MRI adjacency matrices
Whereas MRI produces anatomic information about the brain, functional MRI (fMRI) tells us about neural activity within the brain, including how various regions communicate with each other.
Deep neuroevolution for limited, heterogeneous data: proof-of-concept application to Neuroblastoma brain metastasis using a small virtual pooled image collection
As in prior DNE work, we used a small training set, consisting of 30 normal and 30 metastasis-containing post-contrast MRI brain scans, with 37% outside images.
Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder
We hypothesized that a Deep Reinforcement Learning (DRL) classifier could learn effectively on a small fMRI training set.
Direct evaluation of progression or regression of disease burden in brain metastatic disease with Deep Neuroevolution
We applied the CNNs to our training set, as well as a separate testing set with the same class balance of 25 progression and 25 regression images.
Deep reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumes
Part 2: Then, using these labels, whereas the supervised approach quickly overfit the training data and as expected performed poorly on the testing set (66% accuracy, just over random guessing), the reinforcement learning approach achieved an accuracy of 92%.
Deep Neural Network Based Differential Equation Solver for HIV Enzyme Kinetics
Purpose: We seek to use neural networks (NNs) to solve a well-known system of differential equations describing the balance between T cells and HIV viral burden.
Deep reinforcement learning-based image classification achieves perfect testing set accuracy for MRI brain tumors with a training set of only 30 images
We achieved perfect testing set accuracy with a training set of merely 30 images.
Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training sets
Materials and Methods: We initially clustered images using unsupervised deep learning clustering to generate candidate lesion masks for each MRI image.
Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images
Reinforcement learning predicted testing set lesion locations with 85% accuracy, compared to roughly 7% accuracy for the supervised deep network.
Cross-modality Knowledge Transfer for Prostate Segmentation from CT Scans
By using SynCT images (without segmentation labels) and MR images (with segmentation labels available), we have trained a deep segmentation network for precise delineation of prostate from real CT scans.
