1 citations · 3 across the 8 of their papers we have counts for
5 papers · 1 filter
MedQ-Bench: Evaluating and Exploring Medical Image Quality Assessment Abilities in MLLMs
Jiyao Liu, Jinjie Wei, Wanying Qu +17
Medical Image Quality Assessment (IQA) serves as the first-mile safety gate for clinical AI, yet existing approaches remain constrained by scalar, score-based metrics and fail to r…
UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysis
Junzhi Ning, Wei Li, Cheng Tang +24
Medical workflows routinely combine reading images with producing visual and textual outputs, making both image understanding and generation central to medical AI. Most existing sy…
S2-UniSeg: Fast Universal Agglomerative Pooling for Scalable Segment Anything without Supervision
Huihui Xu, Jin Ye, Hongqiu Wang +10
Recent self-supervised image segmentation models have achieved promising performance on semantic segmentation and class-agnostic instance segmentation. However, their pretraining s…
MedGround-R1: Advancing Medical Image Grounding via Spatial-Semantic Rewarded Group Relative Policy Optimization
Huihui Xu, Yuanpeng Nie, Hualiang Wang +9
Medical Image Grounding (MIG), which involves localizing specific regions in medical images based on textual descriptions, requires models to not only perceive regions but also ded…
Towards Interpretable Counterfactual Generation via Multimodal Autoregression
Chenglong Ma, Yuanfeng Ji, Jin Ye +6
Counterfactual medical image generation enables clinicians to explore clinical hypotheses, such as predicting disease progression, facilitating their decision-making. While existin…