A video-trained large vision model achieves competitive zero-shot performance on organ segmentation, denoising, super-resolution, and 4D CT motion prediction in medical imaging, outperforming some specialized baselines on patient data from 122 cases.
U- net: Convolutional networks for biomedical image segmen- tation
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Are Video Models Emerging as Zero-Shot Learners and Reasoners in Medical Imaging?
A video-trained large vision model achieves competitive zero-shot performance on organ segmentation, denoising, super-resolution, and 4D CT motion prediction in medical imaging, outperforming some specialized baselines on patient data from 122 cases.