Vision language models versus machine learning models performance on polyp detection and classification in colonoscopy images

Abstract Medical image analysis is central to clinical decision-making, and recent advances in vision–language models (VLMs) have introduced promising capabilities for jointly processing visual and textual data. This study evaluates zero-shot VLMs against convolutional neural networks (CNNs) and cla...

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Bibliografische Detailangaben

Hauptverfasser:	Mohammad Amin Khalafi, Seyed Amir Ahmad Safavi-Naini, Ameneh Salehi, Nariman Naderi, Dorsa Alijanzadeh, Pardis Ketabi Moghadam, Kaveh Kavousi, Negar Golestani, Shabnam Shahrokh, Soltanali Fallah, Jamil S. Samaan, Nicholas P. Tatonetti, Nicholas Hoerter, Girish Nadkarni, Hamid Asadzadeh Aghdaei, Ali Soroush
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Nature Portfolio 2025-11-01
Schriftenreihe:	Scientific Reports
Schlagworte:	Vision language models Gastroenterology Computer aided detection Colonoscopy Computer aided diagnosis
Online-Zugang:	https://doi.org/10.1038/s41598-025-29566-2
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