OBJECTIVE
This study investigates how domain-aligned self-supervised pretraining influences downstream PET/CT tumor segmentation, with a focus on disentangling the roles of domain proximity and pretraining dataset scale. Approach: We compared nnU-Net segmentation models initialized from random weights or from DINOv3 en...
Yu-Nong Lin, Shan-Song Wang, Yu-Heng Li et al.· Physics in Medicine and Biol...· 0 citations
BACKGROUND
As curative-intent radiation therapy provides excellent local control for lung tumors, patients are surviving longer to experience treatment-associated adverse cardiac events. Patients treated for left-lung tumors are particularly vulnerable due to the heart's anatomical proximity. While the whole heart is c...
Vanessa L. Wildman, Richard L. J. Qiu, K. Luca et al.· Practical Radiation Oncology· 0 citations
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 resolution and scan time. Diffusion-based super-resolution (SR) reconstructs high-resolution (HR) images from low-resolution (LR) inputs, but...
This review provides a comprehensive and structured synthesis of FMs in medical image analysis by systematically organizing studies into two primary categories: vision-only foundation models (VFMs) and vision-language foundation models (VLFMs), based on their architectural foundations, training strategies, and downstre...
P. Rajendran, M. Safari, Wen-Feng He et al.· Medical Image Analysis· 0 citations
These findings establish radiographic world models as a path toward medical AI that learns clinically meaningful internal states for interpreting, simulating, and constructing evidence for clinical use.
Su-Yang Xi, Song-Tao Hu, Shansong Wang et al.· 4 citations
Results indicate that SSRL-MAR provides a robust and scalable image-domain solution for 3D brain MRI motion correction, enabling reliable structural quantification in large-scale neuroimaging studies without requiring prospectively acquired pairs or acquisition-specific calibration.
M. Safari, Shan-Song Wang, Zach Eidex et al.· IEEE Transactions on Medical...· 0 citations
Adapting a 3D vision-language foundation model for text-guided brain tumor segmentation refinement of glioma subregion segmentations suggests text-dependent contour editing rather than nonspecific post-processing, supporting further evaluation as a clinician-in-the-loop tool.
Zach Eidex, Yunyan Lin, M. Safari et al.· 0 citations
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