| DeepSSM: A blueprint for image-to-shape deep learning models R Bhalodia, S Elhabian, J Adams, W Tao, L Kavan, R Whitaker Medical Image Analysis 91, 103034, 2024 | 26 | 2024 |
| Machine learning in metopic craniosynostosis: does phenotypic severity predict long-term esthetic outcome? JD Blum, J Beiriger, DF Villavisanis, C Morales, DY Cho, W Tao, ... Journal of Craniofacial Surgery 34 (1), 58-64, 2023 | 14 | 2023 |
| Quantifying the severity of metopic craniosynostosis using unsupervised machine learning EE Anstadt, W Tao, E Guo, L Dvoracek, MK Bruce, PJ Grosse, L Wang, ... Plastic and reconstructive surgery 151 (2), 396-403, 2023 | 11 | 2023 |
| 3D photography to quantify the severity of metopic craniosynostosis MK Bruce, W Tao, J Beiriger, C Christensen, MJ Pfaff, R Whitaker, ... The Cleft Palate Craniofacial Journal 60 (8), 971-979, 2023 | 9 | 2023 |
| Validation of artificial intelligence severity assessment in metopic craniosynostosis A Junn, J Dinis, SC Hauc, MK Bruce, KE Park, W Tao, C Christensen, ... The Cleft Palate Craniofacial Journal 60 (3), 274-279, 2023 | 9 | 2023 |
| Relating metopic craniosynostosis severity to intracranial pressure JD Blum, J Beiriger, C Kalmar, RA Avery, SS Lang, DF Villavisanis, ... Journal of Craniofacial Surgery 33 (8), 2372-2378, 2022 | 7 | 2022 |
| Learning population-level shape statistics and anatomy segmentation from images: A joint deep learning model W Tao, R Bhalodia, S Elhabian arXiv preprint arXiv:2201.03481, 2022 | 6 | 2022 |
| Craniorate: An image-based, deep-phenotyping analysis toolset and online clinician interface for metopic craniosynostosis JW Beiriger, W Tao, MK Bruce, E Anstadt, C Christensen, J Smetona, ... Plastic and Reconstructive Surgery 153 (1), 112e-119e, 2023 | 4 | 2023 |
| Unsupervised shape normality metric for severity quantification W Tao, R Bhalodia, E Anstadt, L Kavan, RT Whitaker, JA Goldstein arXiv preprint arXiv:2007.09307, 2020 | 3 | 2020 |
| P64. craniorate: An image-based, deep-phenotyping analysis toolset, repository, and online clinician interface for craniosynostosis J Beiriger, M Bruce, W Tao, R Whitaker, J Goldstein Plastic and Reconstructive Surgery–Global Open 10 (4S), 79, 2022 | 2 | 2022 |
| A Gaussian Process Model for Unsupervised Analysis of High Dimensional Shape Data W Tao, R Bhalodia, R Whitaker Machine Learning in Medical Imaging: 12th International Workshop, MLMI 2021 …, 2021 | 1 | 2021 |
| Review Regularized Neural Collaborative Filtering Z Pan, W Tao, Q Ai arXiv preprint arXiv:2008.13527, 2020 | 1 | 2020 |
| D53. The Spectrum of Severity in Metopic Craniosynostosis: An Analysis of the Largest Cohort to Date using Craniorate™ Machine Learning Algorithm A Dixon, A Glenney, N Kass, J Mocharnuk, C Zhang, E Anstadt, ... Plastic and Reconstructive Surgery–Global Open 12 (S5), 93, 2024 | | 2024 |
| SP43. The Spectrum Of Severity In Metopic Craniosynostosis: An Analysis Of The Largest Cohort To Date Using Craniorate™ Machine Learning Algorithm NM Kass, A Dixon, A Glenney, J Mocharnuk, E Anstadt, L Dvoracek, ... Plastic and Reconstructive Surgery–Global Open 12 (S4), 108, 2024 | | 2024 |
| A Longitudinal Analysis of Pre-and Post-Operative Dysmorphology in Metopic Craniosynostosis JW Beiriger, W Tao, Z Irgebay, J Smetona, L Dvoracek, NM Kass, A Dixon, ... The Cleft Palate Craniofacial Journal, 10556656241237605, 2024 | | 2024 |
| The Spectrum of Severity in 368 Patients with Metopic Craniosynostosis: An Update to the CranioRate™ Machine Learning Algorithm A Glenney, J Mocharnuk, G Bins, E Anstadt, L Dvoracek, W Tao, ... | | 2023 |
| 18. Machine Learning in Metopic Craniosynostosis: Does Phenotypic Severity Predict Long-term Aesthetic Outcome? JD Blum, J Beiriger, C Morales, L Cheung, DF Villavisanis, W Tao, ... Plastic and Reconstructive Surgery–Global Open 10 (6S), 12, 2022 | | 2022 |
| Correction to: A Gaussian Process Model for Unsupervised Analysis of High Dimensional Shape Data W Tao, R Bhalodia, R Whitaker International Workshop on Machine Learning in Medical Imaging, C2-C2, 2021 | | 2021 |
Ross WhitakerProfessor, School of Computing, University of Utah在 cs.utah.edu 的电子邮件经过验证
