1 citations · 1 across the 11 of their papers we have counts for
11 papers
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…
Adaptive Recurrent Message Passing for Test Time Computing on Graphs
Junshu Sun, Wanxing Chang, Qingming Huang +1
Pre-trained foundation models have demonstrated remarkable success in many domains, enabling a unified backbone to generalize across diverse downstream tasks. However, extending th…
Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation
Junshu Sun, Wanxing Chang, Qingming Huang +1
Graph neural networks (GNNs) tightly couple their input-output parameters to dataset-specific feature spaces and target sets, exhibiting limited transferability across different da…
CT-FineBench: A Diagnostic Fidelity Benchmark for Fine-Grained Evaluation of CT Report Generation
Ruifeng Yuan, Wanxing Chang, Weiwei Cao +4
The evaluation of generated reports remains a critical challenge in Computed Tomography (CT) report generation, due to the large volume of text, the diversity and complexity of fin…
Rethinking the Efficiency and Effectiveness of Reinforcement Learning for Radiology Report Generation
Zilin Lu, Ruifeng Yuan, Weiwei Cao +6
Radiologists highly desire fully automated AI for radiology report generation (R2G), yet existing approaches fall short in clinical utility. Reinforcement learning (RL) holds poten…
Relieving the Over-Aggregating Effect in Graph Transformers
Junshu Sun, Wanxing Chang, Chenxue Yang +2
Graph attention has demonstrated superior performance in graph learning tasks. However, learning from global interactions can be challenging due to the large number of nodes. In th…
From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer
Zijiang Yang, Zhongwei Qiu, Tiancheng Lin +13
It is clinically crucial and potentially very beneficial to be able to analyze and model directly the spatial distributions of cells in histopathology whole slide images (WSI). How…
From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba
Zhongwei Qiu, Hanqing Chao, Tiancheng Lin +12
Histopathology plays a critical role in medical diagnostics, with whole slide images (WSIs) offering valuable insights that directly influence clinical decision-making. However, th…