13 citations · 38 across the 19 of their papers we have counts for
21 papers
A radiographic world model for clinical reasoning and evidence generation
Suyang Xi, Songtao Hu, Shansong Wang +8
Medical imaging artificial intelligence (AI) is commonly developed as separate mappings from radiographs to diagnostic outputs or from clinical descriptions to generated images, al…
Motion Artifact-Aware Self-Supervised Representation Learning for 3D Brain MRI Motion Artifact Reduction
Mojtaba Safari, Shansong Wang, Zach Eidex +4
Patient motion remains a source of image degradation in brain MRI, leading to signal loss, blurring, and geometric distortion that compromise quantitative analysis. Existing deep l…
MRI super-resolution in ten sampling steps using a diffusion bridge model
Mojtaba Safari, Hang Yu, Zach Eidex +10
Objective. MRI provides excellent soft-tissue contrast, but long acquisition times can cause patient discomfort and lead to motion artifacts, forcing a trade-off between spatial re…
One-for-All Adaptive Radiotherapy Planning Agent: A Foundation Framework for Daily CBCT-guided Radiotherapy
Shaoyan Pan, Kirk Jon Luca, Yuan Gao +8
In this work, we introduce the One-for-All Adaptive Radiotherapy Planning Agent, a unified foundation-model-based system that performs complete, treatment-specific online adaptive…
Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining
Yuheng Li, Yuan Gao, Haoyu Dong +5
Computed tomography (CT) is a central to three-dimensional medical imaging, yet CT-based artificial intelligence remains fragmented across task-specific models for segmentation, cl…
BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learning
Yizhou Wu, Shansong Wang, Yuheng Li +5
Brain MRI underpins a wide range of neuroscientific and clinical applications, yet most learning-based methods remain task-specific and require substantial labeled data. Here we sh…
MedLVR: Latent Visual Reasoning for Reliable Medical Visual Question Answering
Suyang Xi, Songtao Hu, Yuxiang Lai +4
Medical vision--language models (VLMs) have shown strong potential for medical visual question answering (VQA), yet their reasoning remains largely text-centric: images are encoded…
Evaluating GPT-5 as a Multimodal Clinical Reasoner: A Landscape Commentary
Alexandru Florea, Shansong Wang, Mingzhe Hu +5
The transition from task-specific artificial intelligence toward general-purpose foundation models raises fundamental questions about their capacity to support the integrated reaso…
Efficient Vision Mamba for MRI Super-Resolution via Hybrid Selective Scanning
Mojtaba Safari, Shansong Wang, Vanessa L Wildman +10
Background: High-resolution MRI is critical for diagnosis, but long acquisition times limit clinical use. Super-resolution (SR) can enhance resolution post-scan, yet existing deep…
Low-Dose CT Imaging Using a Regularization-Enhanced Efficient Diffusion Probabilistic Model
Qiang Li, Mojtaba Safari, Shansong Wang +4
Low-dose computed tomography (LDCT) reduces patient radiation exposure but introduces substantial noise that degrades image quality and hinders diagnostic accuracy. Existing denois…
Foundation Models in Medical Image Analysis: A Systematic Review and Meta-Analysis
Praveenbalaji Rajendran, Mojtaba Safari, Wenfeng He +4
Recent advancements in artificial intelligence (AI), particularly foundation models (FMs), have revolutionized medical image analysis, demonstrating strong zero- and few-shot perfo…
DINOv3 with Test-Time Training for Medical Image Registration
Shansong Wang, Mojtaba Safari, Mingzhe Hu +4
Prior medical image registration approaches, particularly learning-based methods, often require large amounts of training data, which constrains clinical adoption. To overcome this…
Benchmarking GPT-5 for Zero-Shot Multimodal Medical Reasoning in Radiology and Radiation Oncology
Mingzhe Hu, Zach Eidex, Shansong Wang +3
Radiology, radiation oncology, and medical physics require decision-making that integrates medical images, textual reports, and quantitative data under high-stakes conditions. With…
Is ChatGPT-5 Ready for Mammogram VQA?
Qiang Li, Shansong Wang, Mingzhe Hu +3
Mammogram visual question answering (VQA) integrates image interpretation with clinical reasoning and has potential to support breast cancer screening. We systematically evaluated…
Performance of GPT-5 in Brain Tumor MRI Reasoning
Mojtaba Safari, Shansong Wang, Mingzhe Hu +3
Accurate differentiation of brain tumor types on magnetic resonance imaging (MRI) is critical for guiding treatment planning in neuro-oncology. Recent advances in large language mo…
Capabilities of GPT-5 on Multimodal Medical Reasoning
Shansong Wang, Mingzhe Hu, Qiang Li +2
Recent advances in large language models (LLMs) have enabled general-purpose systems to perform increasingly complex domain-specific reasoning without extensive fine-tuning. In the…
Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation
Shansong Wang, Zhecheng Jin, Mingzhe Hu +7
CLIP models pretrained on natural images with billion-scale image-text pairs have demonstrated impressive capabilities in zero-shot classification, cross-modal retrieval, and open-…
Res-MoCoDiff: Residual-guided diffusion models for motion artifact correction in brain MRI
Mojtaba Safari, Shansong Wang, Qiang Li +5
Objective. Motion artifacts in brain MRI, mainly from rigid head motion, degrade image quality and hinder downstream applications. Conventional methods to mitigate these artifacts,…
MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting
Mojtaba Safari, Shansong Wang, Zach Eidex +4
Objective:This study introduces a residual error-shifting mechanism that drastically reduces sampling steps while preserving critical anatomical details, thus accelerating MRI reco…
Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging
Shansong Wang, Mojtaba Safari, Qiang Li +5
Vision foundation models (VFMs) are pre-trained on extensive image datasets to learn general representations for diverse types of data. These models can subsequently be fine-tuned…
A Physics-Informed Deep Learning Model for MRI Brain Motion Correction
Mojtaba Safari, Shansong Wang, Zach Eidex +4
Background: MRI is crucial for brain imaging but is highly susceptible to motion artifacts due to long acquisition times. This study introduces PI-MoCoNet, a physics-informed motio…