13 citations · 16 across the 5 of their papers we have counts for
6 papers
Identifying and mitigating bias in algorithms used to manage patients in a pandemic
Yifan Li, Garrett Yoon, Mustafa Nasir-Moin +4
Numerous COVID-19 clinical decision support systems have been developed. However many of these systems do not have the merit for validity due to methodological shortcomings includi…
Patient level simulation and reinforcement learning to discover novel strategies for treating ovarian cancer
Brian Murphy, Mustafa Nasir-Moin, Grace von Oiste +4
The prognosis for patients with epithelial ovarian cancer remains dismal despite improvements in survival for other cancers. Treatment involves multiple lines of chemotherapy and b…
Stereo Video Reconstruction Without Explicit Depth Maps for Endoscopic Surgery
Annika Brundyn, Jesse Swanson, Kyunghyun Cho +2
We introduce the task of stereo video reconstruction or, equivalently, 2D-to-3D video conversion for minimally invasive surgical video. We design and implement a series of end-to-e…
The Utility of General Domain Transfer Learning for Medical Language Tasks
Daniel Ranti, Katie Hanss, Shan Zhao +4
The purpose of this study is to analyze the efficacy of transfer learning techniques and transformer-based models as applied to medical natural language processing (NLP) tasks, spe…
Confounding variables can degrade generalization performance of radiological deep learning models
John R. Zech, Marcus A. Badgeley, Manway Liu +3
Early results in using convolutional neural networks (CNNs) on x-rays to diagnose disease have been promising, but it has not yet been shown that models trained on x-rays from one…
Wide and deep volumetric residual networks for volumetric image classification
Varun Arvind, Anthony Costa, Marcus Badgeley +2
3D shape models that directly classify objects from 3D information have become more widely implementable. Current state of the art models rely on deep convolutional and inception m…