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Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging Research., , , , , , , , , and 1 other author(s). CoRR, (2023)RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models., , , , , , , , , and 7 other author(s). CoRR, (2024)Multitask Brain Tumor Inpainting with Diffusion Models: A Methodological Report., , , , , and . CoRR, (2022)Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients., , , , , , , , , and 6 other author(s). Comput. Biol. Medicine, (2021)COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14, 339 patients., , , , , , , , , and 39 other author(s). Comput. Biol. Medicine, (2022)Few-shot biomedical image segmentation using diffusion models: Beyond image generation., , , , , , , , , and 1 other author(s). Comput. Methods Programs Biomed., (December 2023)A Comparison of Three Different Deep Learning-Based Models to Predict the MGMT Promoter Methylation Status in Glioblastoma Using Brain MRI., , , , and . J. Digit. Imaging, 36 (3): 837-846 (June 2023)Differentiation of COVID-19 pneumonia from other lung diseases using CT radiomic features and machine learning: A large multicentric cohort study., , , , , , , , , and 32 other author(s). Int. J. Imaging Syst. Technol., (March 2024)