44 citations · 107 across the 24 of their papers we have counts for
26 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…
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…
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…
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…
Foundation Models in Medical Image Analysis: A Systematic Review and Meta-Analysis
Praveenbalaji Rajendran, Mojtaba Safari, Wenfeng He +4
Recent advancements in foundation models (FMs) have catalyzed a paradigm shift in medical image analysis. Unlike traditional task-specific artificial intelligence (AI) models, FMs…
MedDINOv3: How to adapt vision foundation models for medical image segmentation?
Yuheng Li, Yizhou Wu, Yuxiang Lai +2
Accurate segmentation of organs and tumors in CT and MRI scans is essential for diagnosis, treatment planning, and disease monitoring. While deep learning has advanced automated se…
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-…
Limited-Angle CBCT Reconstruction via Geometry-Integrated Cycle-domain Denoising Diffusion Probabilistic Models
Yuan Gao, Shaoyan Pan, Mingzhe Hu +6
Cone-beam CT (CBCT) is widely used in clinical radiotherapy for image-guided treatment, improving setup accuracy, adaptive planning, and motion management. However, slow gantry rot…
A Digital Twin Framework for Adaptive Treatment Planning in Radiotherapy
Chih-Wei Chang, Sri Sai Akkineni, Mingzhe Hu +7
The development of a digital twin (DT) framework for fast online adaptive proton therapy planning in prostate stereotactic body radiation therapy (SBRT) with dominant intraprostati…
A Large Convolutional Neural Network for Clinical Target and Multi-organ Segmentation in Gynecologic Brachytherapy with Multi-stage Learning
Mingzhe Hu, Yuan Gao, Yuheng Li +10
Purpose: Accurate segmentation of clinical target volumes (CTV) and organs-at-risk is crucial for optimizing gynecologic brachytherapy (GYN-BT) treatment planning. However, anatomi…
RoMedFormer: A Rotary-Embedding Transformer Foundation Model for 3D Genito-Pelvic Structure Segmentation in MRI and CT
Yuheng Li, Mingzhe Hu, Richard L. J. Qiu +4
Deep learning-based segmentation of genito-pelvic structures in MRI and CT is crucial for applications such as radiation therapy, surgical planning, and disease diagnosis. However,…
Current Progress of Digital Twin Construction Using Medical Imaging
Feng Zhao, Yizhou Wu, Mingzhe Hu +4
Medical imaging has played a pivotal role in advancing and refining digital twin technology, allowing for the development of highly personalized virtual models that represent human…
Mammo-CLIP: Leveraging Contrastive Language-Image Pre-training (CLIP) for Enhanced Breast Cancer Diagnosis with Multi-view Mammography
Xuxin Chen, Yuheng Li, Mingzhe Hu +5
Although fusion of information from multiple views of mammograms plays an important role to increase accuracy of breast cancer detection, developing multi-view mammograms-based com…
Attention-Driven Lightweight Model for Pigmented Skin Lesion Detection
Mingzhe Hu, Xiaofeng Yang
This study presents a lightweight pipeline for skin lesion detection, addressing the challenges posed by imbalanced class distribution and subtle or atypical appearances of some le…
BreastSAM: A Study of Segment Anything Model for Breast Tumor Detection in Ultrasound Images
Mingzhe Hu, Yuheng Li, Xiaofeng Yang
Breast cancer is one of the most common cancers among women worldwide, with early detection significantly increasing survival rates. Ultrasound imaging is a critical diagnostic too…
Polyp-SAM: Transfer SAM for Polyp Segmentation
Yuheng Li, Mingzhe Hu, Xiaofeng Yang
Colon polyps are considered important precursors for colorectal cancer. Automatic segmentation of colon polyps can significantly reduce the misdiagnosis of colon cancer and improve…
SkinSAM: Empowering Skin Cancer Segmentation with Segment Anything Model
Mingzhe Hu, Yuheng Li, Xiaofeng Yang
Skin cancer is a prevalent and potentially fatal disease that requires accurate and efficient diagnosis and treatment. Although manual tracing is the current standard in clinics, a…