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Anytime, Anywhere, Anyone: Investigating the Feasibility of Segment Anything Model for Crowd-Sourcing Medical Image Annotations

Fuente: arXiv

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Dettagli Bibliografici
Autori principali:	Kulkarni, Pranav, Kanhere, Adway, Savani, Dharmam, Chan, Andrew, Chatterjee, Devina, Yi, Paul H., Parekh, Vishwa S.
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

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