RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning

2022279 citationsJournal Articlehybrid Open Access

Authors

Xueyan Mei · Cornell University

Zelong Liu · Cornell University

Philip M. Robson · Cornell University

Brett Marinelli · Cornell University

Mingqian Huang · Cornell University

Amish Doshi · Cornell University

Adam Jacobi · Cornell University

Abstract

RadImageNet pretrained models demonstrated better interpretability compared with ImageNet models, especially for smaller radiologic datasets.<b>Keywords:</b> CT, MR Imaging, US, Head/Neck, Thorax, Brain/Brain Stem, Evidence-based Medicine, Computer Applications-General (Informatics) <i>Supplemental material is available for this article.</i> Published under a CC BY 4.0 license.See also the commentary by Cadrin-Chênevert in this issue.

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Radiomics and Machine Learning in Medical Imaging Artificial Intelligence in Healthcare and Education Lung Cancer Diagnosis and Treatment

Publication Details

Published in: Radiology Artificial Intelligence

Volume 4, Issue 5, pp. e210315-e210315

DOI: 10.1148/ryai.210315

Field-Weighted Citation Impact: 37.98

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RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning

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Abstract

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