7 papers
An Uncertainty-Guided Digital Twin Framework for Online Adaptive Proton Therapy in Head and Neck Cancer: A Feasibility Study
Yizhou Wu, Ryan J. Sanford, Huiqiao Xie +12
Objective: Head and neck (HN) proton therapy spans six to seven weeks of anatomical change, while offline replanning takes about a week. We present an uncertainty-guided digital tw…
A digital-twin framework for forecasting treatment-day imaging with contour uncertainty in adaptive proton radiotherapy
Yizhou Wu, Jie Ding, Justin Roper +6
Head-and-neck anatomy changes over a six-to-seven-week proton course, and the anatomy of a later week cannot be imaged when the plan is made. We present a digital-twin framework th…
Anticipatory Digital Twins for Online Head-and-Neck Adaptive Proton Therapy via Foundation-Model Registration
Yizhou Wu, Yuheng Li, Xiaofeng Yang +1
Head-and-neck (HN) proton therapy is highly sensitive to anatomical change over a 4-to-6-week course, as tumor shrinkage, weight loss, and setup variation can misposition the Bragg…
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…
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…
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…
AnatoMask: Enhancing Medical Image Segmentation with Reconstruction-guided Self-masking
Yuheng Li, Tianyu Luan, Yizhou Wu +3
Due to the scarcity of labeled data, self-supervised learning (SSL) has gained much attention in 3D medical image segmentation, by extracting semantic representations from unlabele…