Predicting Generalization of AI Colonoscopy Models to Unseen Data

\(\textbf{Background}\): 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. \(\textbf{Methods}\): We use a "Masked Siamese Network&...

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Veröffentlicht in:	arXiv.org 2024-03
Hauptverfasser:	Shor, Joel, McNeil, Carson, Intrator, Yotam, Ledsam, Joseph R, Yamano, Hiro-o, Tsurumaru, Daisuke, Kayama, Hiroki, Hamabe, Atsushi, Ando, Koji, Ota, Mitsuhiko, Ogino, Haruei, Nakase, Hiroshi, Kobayashi, Kaho, Miyo, Masaaki, Oki, Eiji, Takemasa, Ichiro, Rivlin, Ehud, Goldenberg, Roman
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial neural networks Colonoscopy Labels Medical imaging Performance evaluation Performance prediction Polyps Sensors Training
Online-Zugang:	Volltext
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