6 citations · 12 across the 8 of their papers we have counts for
4 papers · 1 filter
Deep reinforcement learning with automated label extraction from clinical reports accurately classifies 3D MRI brain volumes
Joseph Stember, Hrithwik Shalu
Purpose: Image classification is perhaps the most fundamental task in imaging AI. However, labeling images is time-consuming and tedious. We have recently demonstrated that reinfor…
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…
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…