1 code implementation • 7 Sep 2023 • Balint Kovacs, Nils Netzer, Michael Baumgartner, Carolin Eith, Dimitrios Bounias, Clara Meinzer, Paul F. Jaeger, Kevin S. Zhang, Ralf Floca, Adrian Schrader, Fabian Isensee, Regula Gnirs, Magdalena Goertz, Viktoria Schuetz, Albrecht Stenzinger, Markus Hohenfellner, Heinz-Peter Schlemmer, Ivo Wolf, David Bonekamp, Klaus H. Maier-Hein
Data augmentation (DA) is a key factor in medical image analysis, such as in prostate cancer (PCa) detection on magnetic resonance images.
no code implementations • 29 Dec 2022 • Vikash Gupta, Barbaros Selnur Erdal, Carolina Ramirez, Ralf Floca, Laurence Jackson, Brad Genereaux, Sidney Bryson, Christopher P Bridge, Jens Kleesiek, Felix Nensa, Rickmer Braren, Khaled Younis, Tobias Penzkofer, Andreas Michael Bucher, Ming Melvin Qin, Gigon Bae, Hyeonhoon Lee, M. Jorge Cardoso, Sebastien Ourselin, Eric Kerfoot, Rahul Choudhury, Richard D. White, Tessa Cook, David Bericat, Matthew Lungren, Risto Haukioja, Haris Shuaib
To address the barrier to clinical deployment, we have formed MONAI Consortium, an open-source community which is building standards for AI deployment in healthcare institutions, and developing tools and infrastructure to facilitate their implementation.
1 code implementation • 4 Nov 2022 • M. Jorge Cardoso, Wenqi Li, Richard Brown, Nic Ma, Eric Kerfoot, Yiheng Wang, Benjamin Murrey, Can Zhao, Dong Yang, Vishwesh Nath, Yufan He, Ziyue Xu, Ali Hatamizadeh, Andriy Myronenko, Wentao Zhu, Yun Liu, Mingxin Zheng, Yucheng Tang, Isaac Yang, Michael Zephyr, Behrooz Hashemian, Sachidanand Alle, Mohammad Zalbagi Darestani, Charlie Budd, Marc Modat, Tom Vercauteren, Guotai Wang, Yiwen Li, Yipeng Hu, Yunguan Fu, Benjamin Gorman, Hans Johnson, Brad Genereaux, Barbaros S. Erdal, Vikash Gupta, Andres Diaz-Pinto, Andre Dourson, Lena Maier-Hein, Paul F. Jaeger, Michael Baumgartner, Jayashree Kalpathy-Cramer, Mona Flores, Justin Kirby, Lee A. D. Cooper, Holger R. Roth, Daguang Xu, David Bericat, Ralf Floca, S. Kevin Zhou, Haris Shuaib, Keyvan Farahani, Klaus H. Maier-Hein, Stephen Aylward, Prerna Dogra, Sebastien Ourselin, Andrew Feng
For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e. g. geometry, physiology, physics) of medical data being processed.