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RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models., , , , , , , , , and 7 other author(s). CoRR, (2024)A Texture Neural Network to Predict the Abnormal Brachial Plexus from Routine Magnetic Resonance Imaging., , , , , , , and . MICCAI (8), volume 14227 of Lecture Notes in Computer Science, page 470-480. Springer, (2023)Toolkits and Libraries for Deep Learning., , , , and . J. Digital Imaging, 30 (4): 400-405 (2017)Paths of Least Flow-Resistance: Characterization for the Optimization of Synthetic Tissue Scaffold Design., and . ISBI, page 606-609. IEEE, (2009)Evaluation of a deep learning architecture for MR imaging prediction of ATRX in glioma patients., , and . Medical Imaging: Computer-Aided Diagnosis, volume 10575 of SPIE Proceedings, page 105752G. SPIE, (2018)Role of Image Acquisition and Patient Phenotype Variations in Automatic Segmentation Model Generalization., , , , and . CoRR, (2023)Reproducibility in medical image radiomic studies: contribution of dynamic histogram binning., , , , and . CoRR, (2022)Predicting 1p19q Chromosomal Deletion of Low-Grade Gliomas from MR Images using Deep Learning., , , , , , , and . CoRR, (2016)Improving Domain Generalization in Segmentation Models with Neural Style Transfer.. ISBI, page 1324-1328. IEEE, (2021)Developing a Machine Learning-Based Clinical Decision Support Tool for Uterine Tumor Imaging., , , , , , , , , and 7 other author(s). CoRR, (2023)