1 citations · 2 across the 4 of their papers we have counts for
5 papers
MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition
Yuhuan Yang, Chaofan Ma, Zhenjie Mao +3
Video understanding is a complex challenge that requires effective modeling of spatial-temporal dynamics. With the success of image foundation models (IFMs) in image understanding,…
AutoMedEval: Harnessing Language Models for Automatic Medical Capability Evaluation
Xiechi Zhang, Zetian Ouyang, Linlin Wang +6
With the proliferation of large language models (LLMs) in the medical domain, there is increasing demand for improved evaluation techniques to assess their capabilities. However, t…
Knowledge-enhanced Pretraining for Vision-language Pathology Foundation Model on Cancer Diagnosis
Xiao Zhou, Luoyi Sun, Dexuan He +10
Vision-language foundation models have shown great promise in computational pathology but remain primarily data-driven, lacking explicit integration of medical knowledge. We introd…
CliMedBench: A Large-Scale Chinese Benchmark for Evaluating Medical Large Language Models in Clinical Scenarios
Zetian Ouyang, Yishuai Qiu, Linlin Wang +4
With the proliferation of Large Language Models (LLMs) in diverse domains, there is a particular need for unified evaluation standards in clinical medical scenarios, where models n…
Towards Evaluating and Building Versatile Large Language Models for Medicine
Chaoyi Wu, Pengcheng Qiu, Jinxin Liu +5
In this study, we present MedS-Bench, a comprehensive benchmark designed to evaluate the performance of large language models (LLMs) in clinical contexts. Unlike existing benchmark…