Martin Lillholm
Professor
Machine Learning
Universitetsparken 1
2100 København Ø
ORCID: 0000-0002-1402-6899
76 - 80 out of 80Page size: 25
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Alzheimer's disease diagnostic performance of a multi-atlas hippocampal segmentation method using the harmonized hippocampal protocol
Anker, C., Sørensen, L., Pai, A. S. U., Lyksborg, M., Lillholm, Martin, Conradsen, K., Larsen, R. & Nielsen, Mads, 2014. 1 p.Research output: Contribution to conference › Conference abstract for conference › Research › peer-review
- Published
A unifying framework for automatic and semi-automatic segmentation of vertebrae from radiographs using sample-driven active shape models
Mysling, P., Petersen, P. K., Nielsen, Mads & Lillholm, Martin, 2013, In: Machine Vision & Applications. 24, 7, p. 1421–1434 14 p.Research output: Contribution to journal › Journal article › Research › peer-review
- Published
A novel OA efficacy marker: cartilage activity
Jørgensen, D. R., Lillholm, Martin & Dam, E. B., 2013, In: Osteoarthritis and Cartilage. 21, Supplement, p. S21-S22 2 p., 28.Research output: Contribution to journal › Conference abstract in journal › Research
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A framework for optimizing measurement weight maps to minimize the required sample size
Qazi, A. A., Jørgensen, D. R., Lillholm, Martin, Loog, M., Nielsen, Mads & Dam, Erik Bjørnager, 2010, In: Medical Image Analysis. 14, 3, p. 255-264 10 p.Research output: Contribution to journal › Journal article › Research › peer-review
A category system on the shape index descriptor of local image structure induced by natural image statistics
Lillholm, Martin & Griffin, L. D., 2006, In: Perception. 35, Supplement, p. 48-49 2 p.Research output: Contribution to journal › Conference abstract in journal › Research
ID: 152298477
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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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626
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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
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338
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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