yngvemm

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Academic interests

  • Biomechanics
  • Fluid dynamics
  • Data science and statistical learning

Courses taught

Background

Having an undergraduate degree in applied mathematics from NMBU, I shifted my Master's degree towards computational biology with a focus on machine learning. Simultaneously, I completed extra modules in mathematics at the University of Oslo. Since then, I have worked at NMBU, researching methods for automatic delineation of tumours from medical images and teaching introductory and advanced courses in Python programming.

Tags: Scientific computing, Biomechanics, Machine learning, Computational medicine

Publications

  • Grøndahl, Aurora Rosvoll; Moe, Yngve Mardal; Kaushal, Christine Kiran; Huynh, Bao Ngoc; Rusten, Espen & Tomic, Oliver [Show all 12 contributors for this article] (2021). Deep learning-based automatic delineation of anal cancer gross tumour volume: a multimodality comparison of CT, PET and MRI. Acta Oncologica. ISSN 0284-186X. 61(1), p. 89–96. doi: 10.1080/0284186X.2021.1994645.
  • Grøndahl, Aurora Rosvoll; Knudtsen, Ingerid Skjei; Huynh, Bao Ngoc; Mulstad, Martine; Moe, Yngve Mardal & Knuth, Franziska [Show all 12 contributors for this article] (2021). A comparison of methods for fully automatic segmentation of tumors and involved nodes in PET/CT of head and neck cancers. Physics in Medicine and Biology. ISSN 0031-9155. 66(6). doi: 10.1088/1361-6560/abe553.
  • Moe, Yngve Mardal; Grøndahl, Aurora Rosvoll; Tomic, Oliver; Dale, Einar; Malinen, Eirik & Futsæther, Cecilia Marie (2021). Deep learning-based auto-delineation of gross tumour volumes and involved nodes in PET/CT images of head and neck cancer patients. European Journal of Nuclear Medicine and Molecular Imaging. ISSN 1619-7070. 48, p. 2782–2792. doi: 10.1007/s00259-020-05125-x. Full text in Research Archive

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  • Huynh, Bao Ngoc; Grøndahl, Aurora Rosvoll; Moe, Yngve Mardal; Tomic, Oliver; Dale, Einar & Malinen, Eirik [Show all 7 contributors for this article] (2021). Deep learning for automatic segmentation of head and neck cancers in PET/CT images: the simpler, the better.
  • Grøndahl, Aurora Rosvoll; Huynh, Bao Ngoc; Moe, Yngve Mardal; Kaushal, Christine Kiran; Rusten, Espen & Tomic, Oliver [Show all 13 contributors for this article] (2021). Deep learning for automatic target volume delineation .
  • Grøndahl, Aurora Rosvoll; Huynh, Bao Ngoc; Moe, Yngve Mardal; Kaushal, Christine Kiran; Rusten, Espen & Tomic, Oliver [Show all 12 contributors for this article] (2021). Deep learning-based automatic delineation of anal cancer gross tumour volume: A multimodality comparison of CT,PET and MRI.
  • Grøndahl, Aurora Rosvoll; Huynh, Bao Ngoc; Moe, Yngve Mardal; Kaushal, Christine Kiran; Rusten, Espen & Tomic, Oliver [Show all 12 contributors for this article] (2021). Deep learning-based automatic delineation of anal cancer gross tumour volume: A multimodality comparison of CT, PET and MRI.
  • Grøndahl, Aurora Rosvoll; Mulstad, Martine; Moe, Yngve Mardal; Knudtsen, Ingerid Skjei; Torheim, Turid Katrine Gjerstad & Tomic, Oliver [Show all 10 contributors for this article] (2019). Comparison of automatic tumour segmentation approaches for head and neck cancers in PET/CT images. Radiotherapy and Oncology. ISSN 0167-8140. 133, p. 557–557.
  • Grøndahl, Aurora Rosvoll; Knudtsen, Ingerid Skjei; Mulstad, Martine; Tomic, Oliver; Moe, Yngve Mardal & Indahl, Ulf Geir [Show all 10 contributors for this article] (2019). Automatic tumour delineation of head and neck cancers in PET/CT images using thresholding and machine learning methods.

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Published Sep. 18, 2020 2:00 PM - Last modified Sep. 22, 2020 1:13 PM