19 citations · 31 across the 3 of their papers we have counts for
3 papers
cs.CV2023★ 12 cited
Augmenting medical image classifiers with synthetic data from latent diffusion models
Luke W. Sagers, James A. Diao, Luke Melas-Kyriazi +6
While hundreds of artificial intelligence (AI) algorithms are now approved or cleared by the US Food and Drugs Administration (FDA), many studies have shown inconsistent generaliza…
eess.IV2022★ 19 cited
Improving dermatology classifiers across populations using images generated by large diffusion models
Luke W. Sagers, James A. Diao, Matthew Groh +3
Dermatological classification algorithms developed without sufficiently diverse training data may generalize poorly across populations. While intentional data collection and annota…
cs.CV2021
PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training
Luke Melas-Kyriazi, Arjun K. Manrai
Unsupervised domain adaptation is a promising technique for semantic segmentation and other computer vision tasks for which large-scale data annotation is costly and time-consuming…