10 citations · 47 across the 20 of their papers we have counts for
4 papers · 1 filter
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…