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…
One-shot synthesis of rare gastrointestinal lesions improves diagnostic accuracy and clinical training
Jia Yu, Yan Zhu, Peiyao Fu +7
Rare gastrointestinal lesions are infrequently encountered in routine endoscopy, restricting the data available for developing reliable artificial intelligence (AI) models and trai…
Robust Polyp Detection and Diagnosis through Compositional Prompt-Guided Diffusion Models
Jia Yu, Yan Zhu, Peiyao Fu +8
Colorectal cancer (CRC) is a significant global health concern, and early detection through screening plays a critical role in reducing mortality. While deep learning models have s…
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…