8 citations · 12 across the 7 of their papers we have counts for
8 papers
PathLang: A Language-Centered Benchmark for Vision-Language Models in Computational Pathology
Fanqi Cheng, Kuo Gong, Shangke Liu +15
Pathology vision-language models (VLMs) have shown strong visual perception ability, but their robustness in the language domain remains poorly characterized. Existing pathology VL…
Location-Aware Fine-Grained Representation Learning for Medical Vision Foundation Models
Myeongkyun Kang, Yanting Yang, Xiaoxiao Li
Fine-grained visual representations are essential for medical image analysis, particularly when diagnostically relevant evidence is subtle and spatially localized. Modern transform…
UltraVR: A Diagnostic Ultra-Resolution Image-VQA Benchmark for Evidence-Grounded Reasoning
Gexin Huang, Yanting Yang, Myeongkyun Kang +6
Vision-language models (VLMs) excel on visual question answering and multimodal reasoning benchmarks. Yet their capability on ultra-resolution images - where critical evidence is t…
Instruction-Free Tuning of Large Vision Language Models for Medical Instruction Following
Myeongkyun Kang, Soopil Kim, Xiaoxiao Li +1
Large vision language models (LVLMs) have demonstrated impressive performance across a wide range of tasks. These capabilities largely stem from visual instruction tuning, which fi…
LoFi: Location-Aware Fine-Grained Representation Learning for Chest X-ray
Myeongkyun Kang, Yanting Yang, Xiaoxiao Li
Fine-grained representation learning is crucial for retrieval and phrase grounding in chest X-rays, where clinically relevant findings are often spatially confined. However, the la…
Can Generalist Vision Language Models (VLMs) Rival Specialist Medical VLMs? Benchmarking and Strategic Insights
Yuan Zhong, Ruinan Jin, Qi Dou +1
Vision Language Models (VLMs) have shown promise in automating image diagnosis and interpretation in clinical settings. However, developing specialist medical VLMs requires substan…
FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models
Ruinan Jin, Zikang Xu, Yuan Zhong +4
The advent of foundation models (FMs) in healthcare offers unprecedented opportunities to enhance medical diagnostics through automated classification and segmentation tasks. Howev…
OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine
Xiaosong Wang, Xiaofan Zhang, Guotai Wang +17
The emerging trend of advancing generalist artificial intelligence, such as GPTv4 and Gemini, has reshaped the landscape of research (academia and industry) in machine learning and…