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Found 5 papers, 1 papers with code
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).
Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT Images
In this study, we introduce a false positive reduction network to overcome this limitation.
Predicting Distant Metastases in Soft-Tissue Sarcomas from PET-CT scans using Constrained Hierarchical Multi-Modality Feature Learning
Most CNNs are designed for single-modality imaging data (CT or PET alone) and do not exploit the information embedded in PET-CT where there is a combination of an anatomical and functional imaging modality.
Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search
'Radiomics' is a method that extracts mineable quantitative features from radiographic images.
