390 citations · 2k across the 86 of their papers we have counts for
87 papers
Med-OPD: Improving Medical Vision-Language Models via Evidence-Aware On-Policy Distillation
Yunhang Qian, Jiaquan Yu, Jiawei Liu +3
Medical Vision-Language Models (Med-VLMs) require reliable reasoning from fine-grained visual evidence, yet existing models can produce plausible clinical answers by relying on lan…
Sparse Representation Learning for Vessels
Chinmay Prabhakar, Bastian Wittmann, Paul Büschl +3
Analyzing human vasculature and vessel-like, tubular structures, such as airways, is crucial for disease diagnosis and treatment. Current methods often rely on small sub-regions or…
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…
MedMASLab: A Unified Orchestration Framework for Benchmarking Multimodal Medical Multi-Agent Systems
Yunhang Qian, Xiaobin Hu, Jiaquan Yu +6
While Multi-Agent Systems (MAS) show potential for complex clinical decision support, the field remains hindered by architectural fragmentation and the lack of standardized multimo…
VariViT: A Vision Transformer for Variable Image Sizes
Aswathi Varma, Suprosanna Shit, Chinmay Prabhakar +5
Vision Transformers (ViTs) have emerged as the state-of-the-art architecture in representation learning, leveraging self-attention mechanisms to excel in various tasks. ViTs split…
Standardized Evaluation of Automatic Methods for Perivascular Spaces Segmentation in MRI -- MICCAI 2024 Challenge Results
Yilei Wu, Yichi Zhang, Zijian Dong +38
Perivascular spaces (PVS), when abnormally enlarged and visible in magnetic resonance imaging (MRI) structural sequences, are important imaging markers of cerebral small vessel dis…
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…
ShortcutBreaker: Low-Rank Noisy Bottleneck and Frequency Filtering Block for Multi-Class Unsupervised Anomaly Detection
Peng Tang, Xiaobin Hu, Tingcheng Li +3
Multi-class unsupervised anomaly detection (MUAD) has garnered growing research interest, as it seeks to develop a unified model for anomaly detection across multiple classes, i.e.…
Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming
Jiazhen Pan, Bailiang Jian, Paul Hager +19
Large language models (LLMs) are increasingly used to answer health-related questions and support healthcare workflows, yet evidence for their safety still relies heavily on static…
Semantically Consistent Discrete Diffusion for 3D Biological Graph Modeling
Chinmay Prabhakar, Suprosanna Shit, Tamaz Amiranashvili +2
3D spatial graphs play a crucial role in biological and clinical research by modeling anatomical networks such as blood vessels,neurons, and airways. However, generating 3D biologi…
BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis
Florian Kofler, Marcel Rosier, Mehdi Astaraki +34
The Brain Tumor Segmentation (BraTS) cluster of challenges has significantly advanced brain tumor image analysis by providing large, curated datasets and addressing clinically rele…
Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge
Vladyslav Zalevskyi, Thomas Sanchez, Misha Kaandorp +67
Accurate fetal brain tissue segmentation and biometric analysis are essential for studying brain development in utero. The FeTA Challenge 2024 advanced automated fetal brain MRI an…
Template-Guided Reconstruction of Pulmonary Segments with Neural Implicit Functions
Kangxian Xie, Yufei Zhu, Kaiming Kuang +4
High-quality 3D reconstruction of pulmonary segments plays a crucial role in segmentectomy and surgical planning for the treatment of lung cancer. Due to the resolution requirement…
Analysis of the MICCAI Brain Tumor Segmentation -- Metastases (BraTS-METS) 2025 Lighthouse Challenge: Brain Metastasis Segmentation on Pre- and Post-treatment MRI
Nazanin Maleki, Raisa Amiruddin, Ahmed W. Moawad +240
Despite continuous advancements in cancer treatment, brain metastatic disease remains a significant complication of primary cancer and is associated with an unfavorable prognosis.…
MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning
Jiazhen Pan, Che Liu, Junde Wu +6
Reasoning is a critical frontier for advancing medical image analysis, where transparency and trustworthiness play a central role in both clinician trust and regulatory approval. A…
GIGP: A Global Information Interacting and Geometric Priors Focusing Framework for Semi-supervised Medical Image Segmentation
Lianyuan Yu, Xiuzhen Guo, Ji Shi +2
Semi-supervised learning enhances medical image segmentation by leveraging unlabeled data, reducing reliance on extensive labeled datasets. On the one hand, the distribution discre…
Semi-Supervised Medical Image Segmentation via Knowledge Mining from Large Models
Yuchen Mao, Hongwei Li, Yinyi Lai +4
Large-scale vision models like SAM have extensive visual knowledge, yet their general nature and computational demands limit their use in specialized tasks like medical image segme…
Unsupervised Low-dose CT Reconstruction with One-way Conditional Normalizing Flows
Ran An, Ke Chen, Hongwei Li
Deep-learning methods have shown promising performance for low-dose computed tomography (LDCT) reconstruction. However, supervised methods face the problem of lacking labeled data…
TV-based Deep 3D Self Super-Resolution for fMRI
Fernando Pérez-Bueno, Hongwei Bran Li, Matthew S. Rosen +3
While functional Magnetic Resonance Imaging (fMRI) offers valuable insights into cognitive processes, its inherent spatial limitations pose challenges for detailed analysis of the…
Learning Brain Tumor Representation in 3D High-Resolution MR Images via Interpretable State Space Models
Qingqiao Hu, Daoan Zhang, Jiebo Luo +4
Learning meaningful and interpretable representations from high-dimensional volumetric magnetic resonance (MR) images is essential for advancing personalized medicine. While Vision…
A Low-dose CT Reconstruction Network Based on TV-regularized OSEM Algorithm
Ran An, Yinghui Zhang, Xi Chen +3
Low-dose computed tomography (LDCT) offers significant advantages in reducing the potential harm to human bodies. However, reducing the X-ray dose in CT scanning often leads to sev…
SeLoRA: Self-Expanding Low-Rank Adaptation of Latent Diffusion Model for Medical Image Synthesis
Yuchen Mao, Hongwei Li, Wei Pang +3
The persistent challenge of medical image synthesis posed by the scarcity of annotated data and the need to synthesize `missing modalities' for multi-modal analysis, underscored th…
3D Vessel Graph Generation Using Denoising Diffusion
Chinmay Prabhakar, Suprosanna Shit, Fabio Musio +5
Blood vessel networks, represented as 3D graphs, help predict disease biomarkers, simulate blood flow, and aid in synthetic image generation, relevant in both clinical and pre-clin…