2 citations · 2 across the 10 of their papers we have counts for
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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…
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…
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 Image Retrieval for Explainable Colorectal Polyp Diagnosis
Ruijie Yang, Yan Zhu, Peiyao Fu +6
Determining the necessity of resecting malignant polyps during colonoscopy screen is crucial for patient outcomes, yet challenging due to the time-consuming and costly nature of hi…
Knowledge Extraction and Distillation from Large-Scale Image-Text Colonoscopy Records Leveraging Large Language and Vision Models
Shuo Wang, Yan Zhu, Xiaoyuan Luo +8
The development of artificial intelligence systems for colonoscopy analysis often necessitates expert-annotated image datasets. However, limitations in dataset size and diversity i…