Nathan M. Cross
University of Washington
23 Papers
21 Citations
Nathan M. Cross is an academic researcher from University of Washington. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 5, co-authored 19 publications.
Chat about Author
Papers
Correction of Motion Artifacts Using a Multiscale Fully Convolutional Neural Network.
TL;DR: Retrospective correction of motion artifacts using a multiscale fully convolutional network is promising and may mitigate the substantial motion-related problems in the clinical MRI workflow.
54
Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs using an Adaptation of the Genant Semiquantitative Criteria.
Qifei Dong,Gang Luo,Nancy E Lane,Li-Yung Lui,Lynn M. Marshall,Deborah M. Kado,Peggy M. Cawthon,Jessica Perry,Sandra K. Johnston,D. Haynor,Jeffrey G. Jarvik,Nathan M. Cross +11 more
TL;DR: In this article , a deep learning classifier for spinal OCFs was developed, which achieved a sensitivity of 59.8%, a PPV of 91.2%, and an F1 score of 0.72.
Avoiding MRI-Related Accidents: A Practical Approach to Implementing MR Safety.
TL;DR: An overview of and the authors' experience with an MRI safety program in terms of risk management and training is presented and the MR safety program requirements and regulations in the United States devised by The Joint Commission and the ACR are discussed.
26
Simulating Tissues with 3D-Printed and Castable Materials
TL;DR: This is the largest study assessing multiple different parameters associated with 3D printing to date and is being made freely available on GitHub, thus affording medical simulation experts access to a database of relevant imaging characteristics of common printable and castable materials.
19
Vaginal Ewing Sarcoma: An Uncommon Clinical Entity in Pediatric Patients
TL;DR: The spectrum of multimodality imaging appearances of Ewing sarcoma at this unusual primary site is described and awareness of vaginal Ewing tumors may facilitate prompt diagnosis and lead to a different surgical approach than the more commonly encountered vaginal rhabdomyosarcoma.
11