Martin Lillholm
Professor
Machine Learning
Universitetsparken 1
2100 København Ø
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
- Published
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
- 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
- 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
An Artificial Intelligence–based Mammography Screening Protocol for Breast Cancer: Outcome and Radiologist Workload
Lauritzen, Andreas, Rodríguez-Ruiz, A., von Euler-Chelpin, My Catarina, Lynge, Elsebeth, Vejborg, I., Nielsen, Mads, Karssemeijer, N. & Lillholm, Martin, 2022, In: Radiology. 304, 1, p. 41-49Research output: Contribution to journal › Journal article › Research › peer-review
- Published
An evaluation of a novel technique for fully automatic synovitis quantification from pre- and post-contrast wrist MRI
Mysling, P., Dam, E., Zaim, S., Genant, H., Fuerst, T. & Lillholm, Martin, 2012, In: Annals of the Rheumatic Diseases. 71, Supplement 3, p. 303-304 2 p., THU0439.Research output: Contribution to journal › Conference abstract in journal › Research › peer-review
- 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
Automated texture scoring for assessing breast cancer masking risk in full field digital mammography
Kallenberg, M. G. J., Petersen, P. K., Lillholm, Martin, Jørgensen, D. R., Diao, P., Holland, K., Karssemeijer, N., Igel, Christian & Nielsen, Mads, 2015, In: Insights into Imaging. 6, 1, Supplement, 1 p., B-0212.Research output: Contribution to journal › Conference abstract in journal › Research › peer-review
- Published
Automatic analysis of trabecular bone structure from knee MRI
Marques, J., Granlund, R., Lillholm, Martin, Pettersen, P. C. & Dam, E. B., 2012, In: Computers in Biology and Medicine. 42, 7, p. 735-742 8 p.Research output: Contribution to journal › Journal article › Research › peer-review
- Published
Automatic quantification of tibio-femoral contact area and congruity
Tummala, S., Nielsen, Mads, Lillholm, Martin, Christiansen, C. & Dam, Erik Bjørnager, 2012, In: I E E E Transactions on Medical Imaging. 31, 7, p. 1404-1412 9 p.Research output: Contribution to journal › Journal article › Research › peer-review
ID: 152298477
Most downloads
-
1625
downloads
Mammographic texture resemblance generalizes as an independent risk factor for breast cancer
Research output: Contribution to journal › Journal article › Research › peer-review
Published -
627
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 -
339
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