6 citations · 18 across the 14 of their papers we have counts for
14 papers
Kinematics Modeling of Peroxy Free Radicals: A Deep Reinforcement Learning Approach
Subhadarsi Nayak, Hrithwik Shalu, Joseph Stember
Tropospheric ozone, known as a concerning air pollutant, has been associated with health issues including asthma, bronchitis, and impaired lung function. The rates at which peroxy…
Uncertainty Quantification in Detecting Choroidal Metastases on MRI via Evolutionary Strategies
Bala McRae-Posani, Andrei Holodny, Hrithwik Shalu +1
Uncertainty quantification plays a vital role in facilitating the practical implementation of AI in radiology by addressing growing concerns around trustworthiness. Given the chall…
Deep neuroevolution to predict primary brain tumor grade from functional MRI adjacency matrices
Joseph Stember, Mehrnaz Jenabi, Luca Pasquini +3
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 eac…
Deep neuroevolution for limited, heterogeneous data: proof-of-concept application to Neuroblastoma brain metastasis using a small virtual pooled image collection
Subhanik Purkayastha, Hrithwik Shalu, David Gutman +6
Artificial intelligence (AI) in radiology has made great strides in recent years, but many hurdles remain. Overfitting and lack of generalizability represent important ongoing chal…
Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder
Joseph Stember, Danielle Stember, Luca Pasquini +3
Purpose : Because functional MRI (fMRI) data sets are in general small, we sought a data efficient approach to resting state fMRI classification of autism spectrum disorder (ASD) v…
Direct evaluation of progression or regression of disease burden in brain metastatic disease with Deep Neuroevolution
Joseph Stember, Robert Young, Hrithwik Shalu
Purpose: A core component of advancing cancer treatment research is assessing response to therapy. Doing so by hand, for example as per RECIST or RANO criteria, is tedious, time-co…
Deep Neuroevolution Squeezes More out of Small Neural Networks and Small Training Sets: Sample Application to MRI Brain Sequence Classification
Joseph N Stember, Hrithwik Shalu
Purpose: Deep Neuroevolution (DNE) holds the promise of providing radiology artificial intelligence (AI) that performs well with small neural networks and small training sets. We s…
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…
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…