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Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels., , , , , , , , , and 18 other author(s). IEEE J. Biomed. Health Informatics, 24 (12): 3529-3538 (2020)End-to-End Lung Nodule Detection in Computed Tomography., , , , and . MLMI@MICCAI, volume 11046 of Lecture Notes in Computer Science, page 37-45. Springer, (2018)Trained Model in Supervised Deep Learning is a Conditional Risk Minimizer., , , and . CoRR, (2022)Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training Samples., , , , and . MICCAI (4), volume 11767 of Lecture Notes in Computer Science, page 741-749. Springer, (2019)Implicit Image-to-Image Schrodinger Bridge for CT Super-Resolution and Denoising., , , , , , , , , and . CoRR, (2024)A Cascaded Convolutional Nerual Network for X-ray Low-dose CT Image Denoising., , , and . CoRR, (2017)Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network., , , and . IEEE Trans. Med. Imaging, 36 (12): 2479-2486 (2017)Development and Validation of a Deep Learning Model for Prediction of Severe Outcomes in Suspected COVID-19 Infection., , , , , , , , , and 1 other author(s). CoRR, (2021)Signal Sensing and Reconstruction Paradigms for a Novel Multi-Source Static Computed Tomography System., , , , , , and . ICASSP, page 9274-9278. IEEE, (2020)Computationally Efficient Cascaded Training for Deep Unrolled Network in CT Imaging., , and . CoRR, (2018)