Predicting the generalization of computer aided detection (CADe) models for colonoscopy

Predicting the generalization of computer aided detection (CADe) models for colonoscopy

Abstract Generalizability of AI colonoscopy algorithms is important for wider adoption in clinical practice. However, current techniques for evaluating performance on unseen data require expensive and time-intensive labels. We show that a "Masked Siamese Network" (MSN), trained to predict...

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Bibliographic Details
Main Authors: Joel Shor, Carson McNeil, Yotam Intrator, Joseph R. Ledsam, Hiro-o Yamano, Daisuke Tsurumaru, Hiroki Kayama, Atsushi Hamabe, Koji Ando, Mitsuhiko Ota, Haruei Ogino, Hiroshi Nakase, Kaho Kobayashi, Masaaki Miyo, Eiji Oki, Ichiro Takemasa, Ehud Rivlin, Roman Goldenberg
Format: Article
Language:English
Published: Springer 2024-11-01
Series:Discover Artificial Intelligence
Subjects:
Image foundational model
Self-supervision
Endoscopy
Computer-aided detection (CADe) system
Artificial intelligence
Online Access:https://doi.org/10.1007/s44163-024-00187-4
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