2 citations · 2 across the 10 of their papers we have counts for
10 papers
SeVeR: Selective Visual Exposure and Retrieval for 3D Medical Image Question Answering
Yaojun Hu, Danyang Tu, Yang Liu +8
Volumetric medical VQA requires reasoning over long and redundant 3D visual token sequences, especially in multi-sequence MRI where complementary modalities provide diverse diagnos…
HounsWorld: A Multimodal World Model for Hidden Patient-State Readout, Reconstruction, and Simulation
Yunhao Bai, Zhongwei Qiu, Guangyu Guo +5
Clinical intelligence requires estimating a patient's underlying condition from incomplete observations rather than learning isolated mappings from scans to answers. Volumetric med…
RadSight: Towards Perceptually Reliable Multimodal Radiology Image Understanding
Jianqin Liu, Weiwei Cao, Wanxing Chang +7
Medical multimodal large language models (MLLMs) are increasingly expected to perform complex image understanding tasks, yet their reliability is often compromised by frequent erro…
Super-Generalist: Towards Comprehensive and Accurate Medical Image Understanding via Generalist-Specialist Synergy
Shaoteng Zhang, Weiwei Cao, Wanxing Chang +9
Medical images require comprehensive and accurate interpretation to support the diagnosis of diverse clincial conditions. Recent vision-language generalist models offer broad task…
AtomiMed: Hierarchical Atomic Fact-Checking for Universal Clinical-Aware Medical Report Evaluation
Yuan Wang, Wanxing Chang, Songtao Jiang +8
Traditional metrics for Medical Report Generation (MRG) predominantly rely on surface-level n-gram overlap, which fails to capture clinical factual accuracy and often overlooks cat…
Disease-Centric Vision-Language Pretraining with Hybrid Visual Encoding for 3D Computed Tomography
Bowen Shi, Weiwei Cao, Ruifeng Yuan +5
Vision-language pre-training (VLP) holds great promise for general-purpose medical AI by leveraging radiology reports as rich textual supervision, yet existing methods struggle wit…
E-MRL: Cross-view Aligned Evidence-driven Multimodal Reinforcement Learning for Reliable 3D Tumor Analysis
Sijing Li, Zhongwei Qiu, Zhuoya Wang +6
While Vision-Language Models (VLMs) show great promise in volumetric medical report generation, they frequently suffer from visual hallucinations and a lack of grounding in 3D CT d…
Visual-OPSD: Cross-Modal On-Policy Self-Distillation for Efficient Unified Multimodal Reasoning
Pengyu Li, Zhitao Gao, Lingling Zhang +5
Unified multimodal models (UMMs) interleave generated ''visual thoughts'' (VTs) with text reasoning to improve spatial tasks. This incurs roughly an order-of-magnitude inference co…
Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training
Weiwei Cao, Jianpeng Zhang, Zhongyi Shui +8
Vision-language pre-training (VLP) has great potential for developing multifunctional and general medical diagnostic capabilities. However, aligning medical images with a low signa…
Large-scale and Fine-grained Vision-language Pre-training for Enhanced CT Image Understanding
Zhongyi Shui, Jianpeng Zhang, Weiwei Cao +8
Artificial intelligence (AI) shows great potential in assisting radiologists to improve the efficiency and accuracy of medical image interpretation and diagnosis. However, a versat…