Self-supervised Learning for Endoscopic Video Analysis

Hirsch, Roy; Caron, Mathilde; Cohen, Regev; Livne, Amir; Shapiro, Ron; Golany, Tomer; Goldenberg, Roman; Freedman, Daniel; Rivlin, Ehud

doi:10.1007/978-3-031-43904-9_55

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Masked Siamese Network (Msn): Compactly Representing Medical...1

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2024

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Cited by 9 publications

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“…We use embeddings described in [27]. These embeddings are trained using the Masked Siamese Networks (MSN) described in [28].…”

Section: Masked Siamese Network (Msn): Compactly Representing Medical...mentioning

confidence: 99%

Predicting Generalization of AI Colonoscopy Models to Unseen Data

Shor,

McNeil,

Intrator

et al. 2024

Preprint

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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 masked out regions of polyp images without labels, can predict the performance of Computer Aided Detection (CADe) of polyps on colonoscopies, without labels. This holds true on Japanese colonoscopies even when MSN is only trained on Israeli colonoscopies, which differ in scoping hardware, endoscope software, screening guidelines, bowel preparation, patient demographics, and the use of techniques such as narrow-band imaging (NBI) and chromoendoscopy (CE). Since our technique uses neither colonoscopy-specific information nor labels, it has the potential to apply to more medical imaging domains.

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“…We use embeddings described in [27]. These embeddings are trained using the Masked Siamese Networks (MSN) described in [28].…”

Section: Masked Siamese Network (Msn): Compactly Representing Medical...mentioning

confidence: 99%