From the 1 of 7 linked papers with an AI index.
7 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…
GI-Bench: A Panoramic Benchmark Revealing the Knowledge-Experience Dissociation of Multimodal Large Language Models in Gastrointestinal Endoscopy Against Clinical Standards
Yan Zhu, Te Luo, Pei-Yao Fu +12
Multimodal Large Language Models (MLLMs) show promise in gastroenterology, yet their performance against comprehensive clinical workflows and human benchmarks remains unverified. T…
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…
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…