Martin Lillholm
Professor
Machine Learning
Universitetsparken 1
2100 København Ø
ORCID: 0000-0002-1402-6899
1 - 1 ud af 1Pr. side: 500
- 2021
- Udgivet
Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients
Jimenez-Solem, E., Petersen, Tonny Studsgaard, Hansen, C., Hansen, C., Lioma, Christina, Igel, Christian, Boomsma, Wouter, Krause, Oswin, Lorenzen, S., Selvan, Raghav, Petersen, Janne, Nyeland, M. E., Ankarfeldt, Mikkel Zöllner, Virenfeldt, G. M., Winther-Jensen, M., Linneberg, Allan René, Mehdipour Ghazi, Mostafa, Detlefsen, N., Lauritzen, Andreas, Smith, Abraham George, de Bruijne, Marleen, Ibragimov, Bulat, Petersen, Jens, Lillholm, Martin, Middleton, Jon Anthony, Mogensen, S. H., Thorsen-Meyer, H., Perner, Anders, Helleberg, M., Kaas-Hansen, Benjamin Skov, Bonde, M., Bonde, A., Pai, A., Nielsen, Mads & Sillesen, Martin Hylleholt, 2021, I: Scientific Reports. 11, 1, 12 s., 3246.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › fagfællebedømt
ID: 152298477
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Mammographic texture resemblance generalizes as an independent risk factor for breast cancer
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Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening: a case-control study
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Automatic segmentation of high-and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative
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