6 citations · 11 across the 4 of their papers we have counts for
6 papers
Deep reinforcement learning-based image classification achieves perfect testing set accuracy for MRI brain tumors with a training set of only 30 images
Joseph Stember, Hrithwik Shalu
Purpose: Image classification may be the fundamental task in imaging artificial intelligence. We have recently shown that reinforcement learning can achieve high accuracy for lesio…
Deep Neural Network Based Differential Equation Solver for HIV Enzyme Kinetics
Joseph Stember, Parvathy Jayan, Hrithwik Shalu
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. Materials and Meth…
Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training sets
Joseph Stember, Hrithwik Shalu
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 fo…
Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets
Joseph N Stember, Hrithwik Shalu
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 lac…
Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images
Joseph Stember, Hrithwik Shalu
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) Inade…
Cross-modality Knowledge Transfer for Prostate Segmentation from CT Scans
Yucheng Liu, Naji Khosravan, Yulin Liu +5
Creating large scale high-quality annotations is a known challenge in medical imaging. In this work, based on the CycleGAN algorithm, we propose leveraging annotations from one mod…