50 citations · 75 across the 5 of their papers we have counts for
5 papers
Not Color Blind: AI Predicts Racial Identity from Black and White Retinal Vessel Segmentations
Aaron S. Coyner, Praveer Singh, James M. Brown +5
Background: Artificial intelligence (AI) may demonstrate racial bias when skin or choroidal pigmentation is present in medical images. Recent studies have shown that convolutional…
CaRENets: Compact and Resource-Efficient CNN for Homomorphic Inference on Encrypted Medical Images
Jin Chao, Ahmad Al Badawi, Balagopal Unnikrishnan +9
Convolutional neural networks (CNNs) have enabled significant performance leaps in medical image classification tasks. However, translating neural network models for clinical appli…
Accelerated Experimental Design for Pairwise Comparisons
Yuan Guo, Jennifer Dy, Deniz Erdogmus +5
Pairwise comparison labels are more informative and less variable than class labels, but generating them poses a challenge: their number grows quadratically in the dataset size. We…
Deep feature transfer between localization and segmentation tasks
Szu-Yeu Hu, Andrew Beers, Ken Chang +8
In this paper, we propose a new pre-training scheme for U-net based image segmentation. We first train the encoding arm as a localization network to predict the center of the targe…
High-resolution medical image synthesis using progressively grown generative adversarial networks
Andrew Beers, James Brown, Ken Chang +4
Generative adversarial networks (GANs) are a class of unsupervised machine learning algorithms that can produce realistic images from randomly-sampled vectors in a multi-dimensiona…