Martin Lillholm
Professor
Machine Learning
Universitetsparken 1
2100 København Ø
ORCID: 0000-0002-1402-6899
1 - 4 out of 4Page size: 50
- 2023
- Published
Assessing Breast Cancer Risk by Combining AI for Lesion Detection and Mammographic Texture
Lauritzen, Andreas, von Euler-Chelpin, My Catarina, Lynge, Elsebeth, Vejborg, I., Nielsen, Mads, Karssemeijer, N. & Lillholm, Martin, 2023, In: Radiology. 308, 2, 8 p., e230227.Research output: Contribution to journal › Journal article › Research › peer-review
- Published
Breast density and risk of breast cancer
Lynge, Elsebeth, Vejborg, I., Lillholm, Martin, Nielsen, Mads, Napolitano, George & von Euler-Chelpin, My Catarina, 2023, In: International Journal of Cancer. 152, 6, p. 1150-1158 9 p.Research output: Contribution to journal › Journal article › Research › peer-review
- Published
Identifying recurrent breast cancer patients in national health registries using machine learning
Lauritzen, Andreas, Berg, T., Jensen, M., Lillholm, Martin & Knoop, A., 2023, In: Acta Oncologica. 62, 4, p. 350–357Research output: Contribution to journal › Journal article › Research › peer-review
- Published
Robust cross-vendor mammographic texture models using augmentation-based domain adaptation for long-term breast cancer risk
Lauritzen, Andreas, von Euler-Chelpin, My Catarina, Lynge, Elsebeth, Vejborg, I., Nielsen, Mads, Karssemeijer, N. & Lillholm, Martin, 2023, In: Journal of Medical Imaging. 10, 5, p. 1-16 054003.Research output: Contribution to journal › Journal article › Research › peer-review
ID: 152298477
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1630
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Mammographic texture resemblance generalizes as an independent risk factor for breast cancer
Research output: Contribution to journal › Journal article › Research › peer-review
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631
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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
Research output: Contribution to journal › Journal article › Research › peer-review
Published -
341
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Automatic segmentation of high-and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative
Research output: Contribution to journal › Journal article › Research › peer-review
Published