5 papers
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…
Verification Mirage: Mapping the Reliability Boundary of Self-Verification in Medical VQA
Ruinan Jin, Beidi Zhao, Myeongkyun Kang +2
Self-verification, re-invoking the same vision language model (VLM) in a fresh context to check its own generated answer, is increasingly used as a default safety layer for medical…
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…