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.
no code implementations • 20 Oct 2022 • Gabriele Campanella, Lucas Kook, Ida Häggström, Torsten Hothorn, Thomas J. Fuchs
An every increasing number of clinical trials features a time-to-event outcome and records non-tabular patient data, such as magnetic resonance imaging or text data in the form of electronic health records.
no code implementations • 21 Jun 2022 • Gabriele Campanella, David Ho, Ida Häggström, Anton S Becker, Jason Chang, Chad Vanderbilt, Thomas J Fuchs
Lung cancer is the leading cause of cancer death worldwide, with lung adenocarcinoma being the most prevalent form of lung cancer.
no code implementations • MIDL 2019 • Chensu Xie, Hassan Muhammad, Chad M. Vanderbilt, Raul Caso, Dig Vijay Kumar Yarlagadda, Gabriele Campanella, Thomas J. Fuchs
A loss with respect to the slide label is backpropagated through an integrated CNN model to $k$ input tiles that are used to represent each part.
no code implementations • 12 Mar 2019 • Hassan Muhammad, Carlie S. Sigel, Gabriele Campanella, Thomas Boerner, Linda M. Pak, Stefan Büttner, Jan N. M. IJzermans, Bas Groot Koerkamp, Michael Doukas, William R. Jarnagin, Amber Simpson, Thomas J. Fuchs
Combinations of these clusters were significant in multivariate analysis.
no code implementations • 17 May 2018 • Gabriele Campanella, Vitor Werneck Krauss Silva, Thomas J. Fuchs
In the field of computational pathology, the use of decision support systems powered by state-of-the-art deep learning solutions has been hampered by the lack of large labeled datasets.
no code implementations • 20 Apr 2018 • Ida Häggström, C. Ross Schmidtlein, Gabriele Campanella, Thomas J. Fuchs
To overcome this problem we present a novel PET image reconstruction technique based on a deep convolutional encoder-decoder network, that takes PET sinogram data as input and directly outputs full PET images.