30 citations · 114 across the 30 of their papers we have counts for
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ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?
Canyu Chen, Jian Yu, Shan Chen +8
Large Language Models (LLMs) hold great promise to revolutionize current clinical systems for their superior capacities on medical text processing tasks and medical licensing exams…
How Does Diverse Interpretability of Textual Prompts Impact Medical Vision-Language Zero-Shot Tasks?
Sicheng Wang, Che Liu, Rossella Arcucci
Recent advancements in medical vision-language pre-training (MedVLP) have significantly enhanced zero-shot medical vision tasks such as image classification by leveraging large-sca…
Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data?
Che Liu, Zhongwei Wan, Haozhe Wang +6
Medical Vision-Language Pre-training (MedVLP) has made significant progress in enabling zero-shot tasks for medical image understanding. However, training MedVLP models typically r…
FMBench: Benchmarking Fairness in Multimodal Large Language Models on Medical Tasks
Peiran Wu, Che Liu, Canyu Chen +3
Advancements in Multimodal Large Language Models (MLLMs) have significantly improved medical task performance, such as Visual Question Answering (VQA) and Report Generation (RG). H…
TorchDA: A Python package for performing data assimilation with deep learning forward and transformation functions
Sibo Cheng, Jinyang Min, Che Liu +1
Data assimilation techniques are often confronted with challenges handling complex high dimensional physical systems, because high precision simulation in complex high dimensional…
LOOK-M: Look-Once Optimization in KV Cache for Efficient Multimodal Long-Context Inference
Zhongwei Wan, Ziang Wu, Che Liu +5
Long-context Multimodal Large Language Models (MLLMs) demand substantial computational resources for inference as the growth of their multimodal Key-Value (KV) cache, in response t…
Argus: Benchmarking and Enhancing Vision-Language Models for 3D Radiology Report Generation
Che Liu, Zhongwei Wan, Yuqi Wang +5
Automatic radiology report generation holds significant potential to streamline the labor-intensive process of report writing by radiologists, particularly for 3D radiographs such…
BIMCV-R: A Landmark Dataset for 3D CT Text-Image Retrieval
Yinda Chen, Che Liu, Xiaoyu Liu +2
The burgeoning integration of 3D medical imaging into healthcare has led to a substantial increase in the workload of medical professionals. To assist clinicians in their diagnosti…
Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement
Che Liu, Zhongwei Wan, Cheng Ouyang +3
Electrocardiograms (ECGs) are non-invasive diagnostic tools crucial for detecting cardiac arrhythmic diseases in clinical practice. While ECG Self-supervised Learning (eSSL) method…
MEIT: Multimodal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation
Zhongwei Wan, Che Liu, Xin Wang +6
Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for monitoring cardiac conditions and is crucial in assisting clinicians. Recent studies have concentrated on cl…
Freeze the backbones: A Parameter-Efficient Contrastive Approach to Robust Medical Vision-Language Pre-training
Jiuming Qin, Che Liu, Sibo Cheng +2
Modern healthcare often utilises radiographic images alongside textual reports for diagnostics, encouraging the use of Vision-Language Self-Supervised Learning (VL-SSL) with large…