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20242026
most citedCARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models

4 citations · 7 across the 11 of their papers we have counts for

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5 papers · 1 filter

cs.CV2024

VHELM: A Holistic Evaluation of Vision Language Models

Tony Lee, Haoqin Tu, Chi Heem Wong +8

Current benchmarks for assessing vision-language models (VLMs) often focus on their perception or problem-solving capabilities and neglect other critical aspects such as fairness,…

cs.CV2024

MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models

Peng Xia, Siwei Han, Shi Qiu +9

Interleaved multimodal comprehension and generation, enabling models to produce and interpret both images and text in arbitrary sequences, have become a pivotal area in multimodal…

cs.CV2024

Generalizing to Unseen Domains in Diabetic Retinopathy with Disentangled Representations

Peng Xia, Ming Hu, Feilong Tang +6

Diabetic Retinopathy (DR), induced by diabetes, poses a significant risk of visual impairment. Accurate and effective grading of DR aids in the treatment of this condition. Yet exi…

cs.LG2024★ 4 cited

CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models

Peng Xia, Ze Chen, Juanxi Tian +21

Artificial intelligence has significantly impacted medical applications, particularly with the advent of Medical Large Vision Language Models (Med-LVLMs), sparking optimism for the…

cs.LG2024★ 3 cited

Multimodal Clinical Trial Outcome Prediction with Large Language Models

Wenhao Zheng, Liaoyaqi Wang, Dongshen Peng +5

The clinical trial is a pivotal and costly process, often spanning multiple years and requiring substantial financial resources. Therefore, the development of clinical trial outcom…