Martin Lillholm

Professor

ORCID: 0000-0002-1402-6899

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2024
Published
Clinical Significance of Combined Density and Deep-Learning-Based Texture Analysis for Stratifying the Risk of Short-Term and Long-Term Breast Cancer in Screening
Vilmun, B. M., Napolitano, George, Lauritzen, A., Lynge, E., Lillholm, Martin, Nielsen, Michael Bachmann & Vejborg, I., 2024, In: Diagnostics. 14, 16, 17 p., 1823.
Research output: Contribution to journal › Journal article › Research › peer-review
Published
Early Indicators of the Impact of Using AI in Mammography Screening for Breast Cancer
Lauritzen, Andreas, Lillholm, Martin, Lynge, Elsebeth, Nielsen, Mads, Karssemeijer, N. & Vejborg, I., 2024, In: Radiology. 311, 3, 9 p., e232479.
Research output: Contribution to journal › Journal article › Research › peer-review

ID: 152298477

1635 downloads
Mammographic texture resemblance generalizes as an independent risk factor for breast cancer
Research output: Contribution to journal › Journal article › Research › peer-review
Published
638 downloads
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
352 downloads
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

Research