no code implementations • 7 Mar 2024 • Gabriele Campanella, Eugene Fluder, Jennifer Zeng, Chad Vanderbilt, Thomas J. Fuchs
Artificial Intelligence (AI) has great potential to improve health outcomes by training systems on vast digitized clinical datasets.
no code implementations • 10 Oct 2023 • Gabriele Campanella, Ricky Kwan, Eugene Fluder, Jennifer Zeng, Aryeh Stock, Brandon Veremis, Alexandros D. Polydorides, Cyrus Hedvat, Adam Schoenfeld, Chad Vanderbilt, Patricia Kovatch, Carlos Cordon-Cardo, Thomas J. Fuchs
Recent breakthroughs in self-supervised learning have enabled the use of large unlabeled datasets to train visual foundation models that can generalize to a variety of downstream tasks.
5 code implementations • 30 Aug 2017 • Li Shen, Laurie R. Margolies, Joseph H. Rothstein, Eugene Fluder, Russell B. McBride, Weiva Sieh
We also demonstrate that a whole image classifier trained using our end-to-end approach on the DDSM digitized film mammograms can be transferred to INbreast FFDM images using only a subset of the INbreast data for fine-tuning and without further reliance on the availability of lesion annotations.
Ranked #15 on Cancer-no cancer per image classification on CBIS-DDSM
Breast Cancer Detection Cancer-no cancer per image classification +1