46 citations · 95 across the 44 of their papers we have counts for
44 papers
MedRSI: Recursive Self-Improvement for Medical Agents via Clinically Aligned Self-Evolution
Junde Wu, Jiayuan Zhu, Minghao Hu +2
Medical agents increasingly combine general reasoning models with specialized clinical tools, yet their capabilities remain largely fixed by what clinicians and engineers design be…
What Makes a Good Medical Image Tokenizer? Rethinking Reconstruction and Generation in Medical Image Tokenization
Niklas Bubeck, Yundi Zhang, Vasiliki Sideri-Lampretsa +4
Latent diffusion models now dominate medical image generation, and every such pipeline rests on a \emph{tokenizer} that compresses images into the latent codes for image generation…
AI Can Be Easily Persuaded in Clinical Decision Making
Jiayuan Zhu, Jiazhen Pan, Fenglin Liu +2
As AI becomes increasingly integrated into clinical practice, it is playing a growing role in medical decision making. Medicine, however, is a high stakes and evidence based field,…
Computing an e-net of a closed hyperbolic surface
Vincent Delecroix, Vincent Despré, Camille Lanuel +2
Hyperbolic surfaces are a fundamental object in mathematics and play an increasingly important role in computational geometry and topology. A key ingredient in the design of effici…
J-CoT: Chain-of-Thought in J-Space
Junde Wu, Jiayuan Zhu, Fengling Liu +2
Chain-of-thought prompting improves language-model reasoning by carrying intermediate states across successive computation steps. However, relying on natural language as the only r…
BioMedArena: An Open-source Toolkit for Building and Evaluating Biomedical Deep Research Agents
Jinge Wu, Hongjian Zhou, Mingde Zeng +8
Reproducing and comparing deep research agents today is hard: the same backbone evaluated on the same benchmark can report different accuracies across papers because the harness an…
Are LLMs Ready to Assist Physicians? PhysAssistBench for Interactive Doctor-Patient-EHR Assistance
Tianming Du, Peijie Yu, Sihan Shang +12
The most plausible near-term role of medical LLMs is to assist rather than replace physicians, yet current evaluations often test isolated capabilities: clinical knowledge, EHR sys…
From Failure to Feedback: Group Revision Unlocks Hard Cases in Object-Level Grounding
Yuyuan Liu, Yiping Ji, Anjie Le +6
Finetuning Large Vision-Language Models with reinforcement learning has emerged as a promising approach to enhance their capability in object-level grounding. However, existing met…
RealICU: Do LLM Agents Understand Long-Context ICU Data? A Benchmark Beyond Behavior Imitation
Chengzhi Shen, Weixiang Shen, Tobias Susetzky +7
Intensive care units (ICU) generate long, dense and evolving streams of clinical information, where physicians must repeatedly reassess patient states under time pressure, undersco…
GRASP: Gated Regression-Aware Skill Proposer for Self-Improving LLM Agents
Johannes Moll, Jean-Philippe Corbeil, Jiazhen Pan +4
LLM agents acting in structured environments fail in operational rather than conversational ways, and reliability depends on procedural knowledge of the environment. Prior self-imp…
Dynamic Decision Learning: Test-Time Evolution for Abnormality Grounding in Rare Diseases
Jun Li, Mingxuan Liu, Jiazhen Pan +4
Clinical abnormality grounding for rare diseases is often hindered by data scarcity, making supervised fine-tuning impractical and single-pass inference highly unstable. We propose…
Evo-MedAgent: Beyond One-Shot Diagnosis with Agents That Remember, Reflect, and Improve
Weixiang Shen, Bailiang Jian, Jun Li +6
Tool-augmented large language model (LLM) agents can orchestrate specialist classifiers, segmentation models, and visual question-answering modules to interpret chest X-rays. Howev…
Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysis
Ayhan Can Erdur, Daniel Scholz, Jiazhen Pan +3
State-of-the-art large language models (LLMs) show high performance in general visual question answering. However, a fundamental limitation remains: current architectures lack the…
Opportunistic Cardiac Health Assessment: Estimating Phenotypes from Localizer MRI through Multi-Modal Representations
Busra Nur Zeybek, Özgün Turgut, Yundi Zhang +5
Cardiovascular diseases are the leading cause of death. Cardiac phenotypes (CPs), e.g., ejection fraction, are the gold standard for assessing cardiac health, but they are derived…
No Image, No Problem: End-to-End Multi-Task Cardiac Analysis from Undersampled k-Space
Yundi Zhang, Sevgi Gokce Kafali, Niklas Bubeck +2
Conventional clinical CMR pipelines rely on a sequential "reconstruct-then-analyze" paradigm, forcing an ill-posed intermediate step that introduces avoidable artifacts and informa…
MedOpenClaw and MedFlowBench: Auditing Medical Agents in Full-Study Workflows
Weixiang Shen, Chengzhi Shen, Yanzhu Hu +12
Medical imaging benchmarks often evaluate VLMs on pre-selected 2D images, slices, crops, or patches, making evaluation closer to visual recognition. Real clinical workflows impose…
Evo: Autoregressive-Diffusion Large Language Models with Evolving Balance
Junde Wu, Minhao Hu, Jiayuan Zhu +7
We introduce \textbf{Evo}, a duality latent trajectory model that bridges autoregressive (AR) and diffusion-based language generation within a continuous evolutionary generative fr…
SWE Context Bench: A Benchmark for Context Learning in Coding
Jiayuan Zhu, Junde Wu, Minhao Hu +9
Large language models are increasingly used as coding agents for software engineering tasks. Current benchmarks mainly evaluate whether the agent can correctly solve the request or…
Disentangling Progress in Medical Image Registration: Beyond Trend-Driven Architectures towards Domain-Specific Strategies
Bailiang Jian, Jiazhen Pan, Rohit Jena +5
Medical image registration drives quantitative analysis across organs, modalities, and patient populations. Recent deep learning methods often combine low-level "trend-driven" comp…
Med-CMR: A Fine-Grained Benchmark Integrating Visual Evidence and Clinical Logic for Medical Complex Multimodal Reasoning
Haozhen Gong, Xiaozhong Ji, Yuansen Liu +9
MLLMs MLLMs are beginning to appear in clinical workflows, but their ability to perform complex medical reasoning remains unclear. We present Med-CMR, a fine-grained Medical Comple…
Evaluating Reasoning Faithfulness in Medical Vision-Language Models using Multimodal Perturbations
Johannes Moll, Markus Graf, Tristan Lemke +7
Vision-language models (VLMs) often produce chain-of-thought (CoT) explanations that sound plausible yet fail to reflect the underlying decision process, undermining trust in high-…
Unsupervised whole-heart function assessment
Yundi Zhang, Daniel Rueckert, Jiazhen Pan
Motivation: CMR is the golden standard for cardiac diagnosis, and medical data annotation is time-consuming. Thus, screening techniques from unlabeled data can help streamline the…
Reconstruction-free segmentation from undersampled k-space using transformers
Yundi Zhang, Nil Stolt-Ansó, Jiazhen Pan +3
Motivation: High acceleration factors place a limit on MRI image reconstruction. This limit is extended to segmentation models when treating these as subsequent independent process…
MultiMAE for Brain MRIs: Robustness to Missing Inputs Using Multi-Modal Masked Autoencoder
Ayhan Can Erdur, Christian Beischl, Daniel Scholz +4
Missing input sequences are common in medical imaging data, posing a challenge for deep learning models reliant on complete input data. In this work, inspired by MultiMAE [2], we d…