Nathaniel Swinburne
Affiliations: | Radiology | Memorial Sloan Kettering Cancer Center, Rockville Centre, NY, United States |
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Sign in to add traineeVivek Yadav | research assistant | 2020- | Memorial Sloan Kettering Cancer Center |
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Publications
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Swinburne NC, Jackson CB, Pagano AM, et al. (2024) Foundational Segmentation Models and Clinical Data Mining Enable Accurate Computer Vision for Lung Cancer. Journal of Imaging Informatics in Medicine |
Hsu DG, Ballangrud Å, Prezelski K, et al. (2023) Automatically tracking brain metastases after stereotactic radiosurgery. Physics and Imaging in Radiation Oncology. 27: 100452 |
Santilli A, Panyam P, Autz A, et al. (2023) Automated full body tumor segmentation in DOTATATE PET/CT for neuroendocrine cancer patients. International Journal of Computer Assisted Radiology and Surgery |
Swinburne NC, Yadav V, Murthy KNK, et al. (2023) Fast, light, and scalable: harnessing data-mined line annotations for automated tumor segmentation on brain MRI. European Radiology |
Huang Y, Moreno R, Malani R, et al. (2022) Deep Learning Achieves Neuroradiologist-Level Performance in Detecting Hydrocephalus Requiring Treatment. Journal of Digital Imaging |
Swinburne NC, Yadav V, Kim J, et al. (2022) Semisupervised Training of a Brain MRI Tumor Detection Model Using Mined Annotations. Radiology. 210817 |
Swinburne NC, Schefflein J, Sakai Y, et al. (2019) Machine learning for semi-automated classification of glioblastoma, brain metastasis and central nervous system lymphoma using magnetic resonance advanced imaging. Annals of Translational Medicine. 7: 232 |
Swinburne NC, Mendelson D, Rubin DL. (2019) Advancing Semantic Interoperability of Image Annotations: Automated Conversion of Non-standard Image Annotations in a Commercial PACS to the Annotation and Image Markup. Journal of Digital Imaging |