4 citations · 11 across the 24 of their papers we have counts for
25 papers
DeVAR: Low-Dose CT Denoising via Visual Autoregressive Modeling
Xizhuo Zhang, Yannian Gu, Zhongzhen Huang +2
Computed tomography (CT) plays a crucial role in medical diagnosis, but minimizing radiation exposure while maintaining image quality remains a critical challenge. Low-dose CT (LDC…
MedExpMem: Adapting Experience Memory for Differential Diagnosis
Qianhan Feng, Zhongzhen Huang, Yakun Zhu +4
Experienced physicians develop diagnostic expertise through clinical practice, acquiring not only disease knowledge but also the ability to differentiate confusable conditions. Cur…
Human-AI Co-reasoning for Clinical Diagnosis with Evidence-Integrated Language Agent
Zhongzhen Huang, Yan Ling, Hong Chen +7
We present PULSE, a medical reasoning agent that combines a domain-tuned large language model with scientific literature retrieval to support diagnostic decision-making in complex…
CURE: A Multimodal Benchmark for Clinical Understanding and Retrieval Evaluation
Yannian Gu, Zhongzhen Huang, Linjie Mu +3
Multimodal large language models (MLLMs) demonstrate considerable potential in clinical diagnostics, a domain that inherently requires synthesizing complex visual and textual data…
CT-Flow: Orchestrating CT Interpretation Workflow with Model Context Protocol Servers
Yannian Gu, Xizhuo Zhang, Linjie Mu +4
Recent advances in Large Vision-Language Models (LVLMs) have shown strong potential for multi-modal radiological reasoning, particularly in tasks like diagnostic visual question an…
EHRWorld: A Patient-Centric Medical World Model for Long-Horizon Clinical Trajectories
Linjie Mu, Zhongzhen Huang, Yannian Gu +3
World models offer a principled framework for simulating future states under interventions, but realizing such models in complex, high-stakes domains like medicine remains challeng…
MedMCP-Calc: Benchmarking LLMs for Realistic Medical Calculator Scenarios via MCP Integration
Yakun Zhu, Yutong Huang, Shengqian Qin +3
Medical calculators are fundamental to quantitative, evidence-based clinical practice. However, their real-world use is an adaptive, multi-stage process, requiring proactive EHR da…
PathFound: An Agentic Multimodal Model Activating Evidence-seeking Pathological Diagnosis
Shengyi Hua, Jianfeng Wu, Tianle Shen +7
Recent pathological foundation models have substantially advanced visual representation learning and multimodal interaction. However, most models still rely on a static inference p…
MedCEG: Reinforcing Verifiable Medical Reasoning with Critical Evidence Graph
Linjie Mu, Yannian Gu, Zhongzhen Huang +3
Large language models with reasoning capabilities have demonstrated impressive performance across a wide range of domains. In clinical applications, a transparent, step-by-step rea…
CP-Env: Evaluating Large Language Models on Clinical Pathways in a Controllable Hospital Environment
Yakun Zhu, Zhongzhen Huang, Qianhan Feng +5
Medical care follows complex clinical pathways that extend beyond isolated physician-patient encounters, emphasizing decision-making and transitions between different stages. Curre…
UCAgents: Unidirectional Convergence for Visual Evidence Anchored Multi-Agent Medical Decision-Making
Qianhan Feng, Zhongzhen Huang, Yakun Zhu +2
Vision-Language Models (VLMs) show promise in medical diagnosis, yet suffer from reasoning detachment, where linguistically fluent explanations drift from verifiable image evidence…
Radiologist Copilot: An Agentic Framework Orchestrating Specialized Tools for Reliable Radiology Reporting
Yongrui Yu, Zhongzhen Huang, Linjie Mu +2
In clinical practice, radiology reporting is an essential yet complex, time-intensive, and error-prone task, particularly for 3D medical images. Existing automated approaches based…
SAP-Bench: Benchmarking Multimodal Large Language Models in Surgical Action Planning
Mengya Xu, Zhongzhen Huang, Dillan Imans +3
Effective evaluation is critical for driving advancements in MLLM research. The surgical action planning (SAP) task, which aims to generate future action sequences from visual inpu…
Elicit and Enhance: Advancing Multimodal Reasoning in Medical Scenarios
Zhongzhen Huang, Linjie Mu, Yakun Zhu +3
Effective clinical decision-making depends on iterative, multimodal reasoning across diverse sources of evidence. The recent emergence of multimodal reasoning models has significan…
DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models
Yakun Zhu, Zhongzhen Huang, Linjie Mu +6
The emergence of groundbreaking large language models capable of performing complex reasoning tasks holds significant promise for addressing various scientific challenges, includin…
MMXU: A Multi-Modal and Multi-X-ray Understanding Dataset for Disease Progression
Linjie Mu, Zhongzhen Huang, Shengqian Qin +3
Large vision-language models (LVLMs) have shown great promise in medical applications, particularly in visual question answering (MedVQA) and diagnosis from medical images. However…
O1 Replication Journey -- Part 3: Inference-time Scaling for Medical Reasoning
Zhongzhen Huang, Gui Geng, Shengyi Hua +5
Building upon our previous investigations of O1 replication (Part 1: Journey Learning [Qin et al., 2024] and Part 2: Distillation [Huang et al., 2024]), this work explores the pote…
CAT: Coordinating Anatomical-Textual Prompts for Multi-Organ and Tumor Segmentation
Zhongzhen Huang, Yankai Jiang, Rongzhao Zhang +2
Existing promptable segmentation methods in the medical imaging field primarily consider either textual or visual prompts to segment relevant objects, yet they often fall short whe…
Tool Calling: Enhancing Medication Consultation via Retrieval-Augmented Large Language Models
Zhongzhen Huang, Kui Xue, Yongqi Fan +5
Large-scale language models (LLMs) have achieved remarkable success across various language tasks but suffer from hallucinations and temporal misalignment. To mitigate these shortc…
Grounded Knowledge-Enhanced Medical Vision-Language Pre-training for Chest X-Ray
Qiao Deng, Zhongzhen Huang, Yunqi Wang +6
Medical foundation models have the potential to revolutionize healthcare by providing robust and generalized representations of medical data. Medical vision-language pre-training h…
Modality-Aware and Shift Mixer for Multi-modal Brain Tumor Segmentation
Zhongzhen Huang, Linda Wei, Shaoting Zhang +1
Combining images from multi-modalities is beneficial to explore various information in computer vision, especially in the medical domain. As an essential part of clinical diagnosis…
ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-Prompting
Yankai Jiang, Zhongzhen Huang, Rongzhao Zhang +2
The long-tailed distribution problem in medical image analysis reflects a high prevalence of common conditions and a low prevalence of rare ones, which poses a significant challeng…
KiUT: Knowledge-injected U-Transformer for Radiology Report Generation
Zhongzhen Huang, Xiaofan Zhang, Shaoting Zhang
Radiology report generation aims to automatically generate a clinically accurate and coherent paragraph from the X-ray image, which could relieve radiologists from the heavy burden…
One for All: One-stage Referring Expression Comprehension with Dynamic Reasoning
Zhipeng Zhang, Zhimin Wei, Zhongzhen Huang +2
Referring Expression Comprehension (REC) is one of the most important tasks in visual reasoning that requires a model to detect the target object referred by a natural language exp…