The unreasonable effectiveness of AI CADe polyp detectors to generalize to new countries

no code implementations • 11 Dec 2023 • Joel Shor, Hiro-o Yamano, Daisuke Tsurumaru, Yotami Intrator, Hiroki Kayama, Joe Ledsam, Atsushi Hamabe, Koji Ando, Mitsuhiko Ota, Haruei Ogino, Hiroshi Nakase, Kaho Kobayashi, Eiji Oki, Roman Goldenberg, Ehud Rivlin, Ichiro Takemasa

$\textbf{Conclusion}$: Differences that prevent CADe detectors from performing well in non-medical settings do not degrade the performance of our AI CADe polyp detector when applied to data from a new country.

Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning

no code implementations • 18 Oct 2018 • Avinash Varadarajan, Pinal Bavishi, Paisan Raumviboonsuk, Peranut Chotcomwongse, Subhashini Venugopalan, Arunachalam Narayanaswamy, Jorge Cuadros, Kuniyoshi Kanai, George Bresnick, Mongkol Tadarati, Sukhum Silpa-archa, Jirawut Limwattanayingyong, Variya Nganthavee, Joe Ledsam, Pearse A. Keane, Greg S. Corrado, Lily Peng, Dale R. Webster

To improve the accuracy of DME screening, we trained a deep learning model to use color fundus photographs to predict ci-DME.

Specificity

Deep learning for predicting refractive error from retinal fundus images

no code implementations • 21 Dec 2017 • Avinash V. Varadarajan, Ryan Poplin, Katy Blumer, Christof Angermueller, Joe Ledsam, Reena Chopra, Pearse A. Keane, Greg S. Corrado, Lily Peng, Dale R. Webster

Mean absolute error (MAE) of the algorithm's prediction compared to the refractive error obtained in the AREDS and UK Biobank.

Deep Learning