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Found 3 papers, 1 papers with code
Full-resolution MLPs Empower Medical Dense Prediction
This textural information is crucial for medical dense prediction as it can differentiate the subtle human anatomy in medical images.
PET Synthesis via Self-supervised Adaptive Residual Estimation Generative Adversarial Network
We introduce (1) An adaptive residual estimation mapping mechanism, AE-Net, designed to dynamically rectify the preliminary synthesized PET images by taking the residual map between the low-dose PET and synthesized output as the input, and (2) A self-supervised pre-training strategy to enhance the feature representation of the coarse generator.
CG-3DSRGAN: A classification guided 3D generative adversarial network for image quality recovery from low-dose PET images
We compared our method to the state-of-the-art methods on whole-body PET with different dose reduction factors (DRFs).
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