Search Results for author: Andrew B. Sellergren

Found 2 papers, 2 papers with code

Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging Research

1 code implementation • 15 Nov 2023 • Bardia Khosravi, Frank Li, Theo Dapamede, Pouria Rouzrokh, Cooper U. Gamble, Hari M. Trivedi, Cody C. Wyles, Andrew B. Sellergren, Saptarshi Purkayastha, Bradley J. Erickson, Judy W. Gichoya

This study examines the impact of synthetic data supplementation, using diffusion models, on the performance of deep learning (DL) classifiers for CXR analysis.

Denoising

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Code

Simplified Transfer Learning for Chest Radiography Models Using Less Data

1 code implementation • Radiology 2022 • Andrew B. Sellergren, Christina Chen, Zaid Nabulsi, Yuanzhen Li, Aaron Maschinot, Aaron Sarna, Jenny Huang, Charles Lau, Sreenivasa Raju Kalidindi, Mozziyar Etemadi, Florencia Garcia-Vicente, David Melnick, Yun Liu, Krish Eswaran, Daniel Tse, Neeral Beladia, Dilip Krishnan, Shravya Shetty

Supervised contrastive learning enabled performance comparable to state-of-the-art deep learning models in multiple clinical tasks by using as few as 45 images and is a promising method for predictive modeling with use of small data sets and for predicting outcomes in shifting patient populations.

Contrastive Learning Transfer Learning

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Code

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