1.7k citations · 1.7k across the 4 of their papers we have counts for
4 papers
Individual predictions matter: Assessing the effect of data ordering in training fine-tuned CNNs for medical imaging
John R. Zech, Jessica Zosa Forde, Michael L. Littman
We reproduced the results of CheXNet with fixed hyperparameters and 50 different random seeds to identify 14 finding in chest radiographs (x-rays). Because CheXNet fine-tunes a pre…
Deep Learning Predicts Hip Fracture using Confounding Patient and Healthcare Variables
Marcus A. Badgeley, John R. Zech, Luke Oakden-Rayner +7
Hip fractures are a leading cause of death and disability among older adults. Hip fractures are also the most commonly missed diagnosis on pelvic radiographs. Computer-Aided Diagno…
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…
Inferring Team Strengths Using a Discrete Markov Random Field
John Zech, Frank Wood
We propose an original model for inferring team strengths using a Markov Random Field, which can be used to generate historical estimates of the offensive and defensive strengths o…