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Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance., , , , , , , , , and 6 other author(s). J. Digit. Imaging, 34 (4): 1049-1058 (2021)A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network., , , , , , , , , and 30 other author(s). CoRR, (2023)Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI., , , , , , , , , and 5 other author(s). CoRR, (2022)Automated segmentation of pediatric brain tumors based on multi-parametric MRI and deep learning., , , , , , , , , and 10 other author(s). Medical Imaging: Computer-Aided Diagnosis, volume 12033 of SPIE Proceedings, SPIE, (2022)DEEPMIR: A DEEP convolutional neural network for differential detection of cerebral Microbleeds and IRon deposits in MRI., , , , , , , , and . CoRR, (2020)The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)., , , , , , , , , and 52 other author(s). CoRR, (2023)Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors., , , , , , , , , and 5 other author(s). CoRR, (2024)Adaptive Squeeze-and-Shrink Image Denoising for Improving Deep Detection of Cerebral Microbleeds., , , , , , , , and . MICCAI (6), volume 12906 of Lecture Notes in Computer Science, page 265-275. Springer, (2021)