From the 1 of 4 linked papers with an AI index.
4 papers
A report-grounded vision-language foundation model for colonoscopy from 280000 routine reports
Jia Yu, Yan Zhu, Yili He +12
The paper presents EndoCLIP, a vision‑language foundation model for colonoscopy that learns from lesion‑level image‑text pairs extracted from routine colonoscopy reports, achieving…
Development and multi-center evaluation of domain-adapted speech recognition for human-AI teaming in real-world gastrointestinal endoscopy
Ruijie Yang, Yan Zhu, Peiyao Fu +6
Automatic speech recognition (ASR) is a critical interface for human-AI interaction in gastrointestinal endoscopy, yet its reliability in real-world clinical settings is limited by…
EndoFinder: Online Lesion Retrieval for Explainable Colorectal Polyp Diagnosis Leveraging Latent Scene Representations
Ruijie Yang, Yan Zhu, Peiyao Fu +6
Colorectal cancer (CRC) remains a leading cause of cancer-related mortality, underscoring the importance of timely polyp detection and diagnosis. While deep learning models have im…
Endo-CLIP: Progressive Self-Supervised Pre-training on Raw Colonoscopy Records
Yili He, Yan Zhu, Peiyao Fu +7
Pre-training on image-text colonoscopy records offers substantial potential for improving endoscopic image analysis, but faces challenges including non-informative background image…