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Journal of Medical Imaging (Bellingham, Wash.)|January 19, 2017
Fully automated quantitative cephalometry using convolutional neural networksSercan Ö Arık, Bulat Ibragimov, Lei XingJournal of Medical Imaging (Bellingham, Wash.)|March 28, 2019
Three-dimensionally-printed anthropomorphic physical phantom for mammography and digital breast tomosynthesis with custom materials, lesions, and uniform quality control regionAndrea H Rossman, Matthew Catenacci, Christine Zhao, et al.Journal of Medical Imaging (Bellingham, Wash.)|March 2, 2021
Automated workflow for volumetric assessment of signal intensity ratio on T1-weighted MR images after multiple gadolinium administrationsChia-Ying Liu, Marc Ramos, David Moreno-Dominguez, et al.Journal of Medical Imaging (Bellingham, Wash.)|March 18, 2021
Training focal lung pathology detection using an eye movement modeling exampleStephanie Brams, Gal Ziv, Ignace Tc Hooge, et al.Journal of Medical Imaging (Bellingham, Wash.)|March 18, 2021
Sinogram + image domain neural network approach for metal artifact reduction in low-dose cone-beam computed tomographyMichael D Ketcha, Michael Marrama, Andre Souza, et al.Journal of Medical Imaging (Bellingham, Wash.)|November 8, 2021
Influence of background preprocessing on the performance of deep learning retinal vessel detectionJames Owler, Peter RockettJournal of Medical Imaging (Bellingham, Wash.)|February 4, 2022
Brain tumor IDH, 1p/19q, and MGMT molecular classification using MRI-based deep learning: an initial study on the effect of motion and motion correctionSahil S Nalawade, Fang F Yu, Chandan Ganesh Bangalore Yogananda, et al.Journal of Medical Imaging (Bellingham, Wash.)|July 18, 2022
Learning-based three-dimensional registration with weak bounding box supervisionMona Schumacher, Hanna Siebert, Andreas Genz, et al.Journal of Medical Imaging (Bellingham, Wash.)|September 30, 2021
Deep learning-based segmentation of the placenta and uterus on MR imagesMaysam Shahedi, Catherine Y Spong, James D Dormer, et al.Journal of Medical Imaging (Bellingham, Wash.)|October 3, 2018
Evaluation of deep learning methods for parotid gland segmentation from CT imagesAnnika Hänsch, Michael Schwier, Tobias Gass, et al.Pageof 152