5 papers
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…
MLB: A Scenario-Driven Benchmark for Evaluating Large Language Models in Clinical Applications
Qing He, Dongsheng Bi, Jianrong Lu +20
The proliferation of Large Language Models (LLMs) presents transformative potential for healthcare, yet practical deployment is hindered by the absence of frameworks that assess re…
AnyCXR: Human Anatomy Segmentation of Chest X-ray at Any Acquisition Position using Multi-stage Domain Randomized Synthetic Data with Imperfect Annotations and Conditional Joint Annotation Regularization Learning
Zifei Dong, Wenjie Wu, Jinkui Hao +3
Robust anatomical segmentation of chest X-rays (CXRs) remains challenging due to the scarcity of comprehensive annotations and the substantial variability of real-world acquisition…
CLIP Based Region-Aware Feature Fusion for Automated BBPS Scoring in Colonoscopy Images
Yujia Fu, Zhiyu Dong, Tianwen Qian +3
Accurate assessment of bowel cleanliness is essential for effective colonoscopy procedures. The Boston Bowel Preparation Scale (BBPS) offers a standardized scoring system but suffe…
Refine Medical Diagnosis Using Generation Augmented Retrieval and Clinical Practice Guidelines
Wenhao Li, Hongkuan Zhang, Hongwei Zhang +5
Current medical language models, adapted from large language models (LLMs), typically predict ICD code-based diagnosis from electronic health records (EHRs) because these labels ar…