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
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
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
Assessing breast cancer masking risk in full field digital mammography with automated texture analysis
Kallenberg, M. G. J., Lillholm, Martin, Diao, P., Holland, K., Karssemeijer, N., Igel, Christian & Nielsen, Mads, 2015, 7th International Workshop on Breast Densitometry and Cancer Risk Assessment (Non-CME). University of California, p. 109 1 p.Research output: Chapter in Book/Report/Conference proceeding › Conference abstract in proceedings › Research › peer-review
- Published
Assessing breast cancer masking risk with automated texture analysis in full field digital mammography
Kallenberg, M. G. J., Lillholm, Martin, Diao, P., Petersen, K., Holland, K., Karssemeijer, N., Igel, Christian & Nielsen, Mads, 2015, Breast Imaging and Interventional. Radiological Society of North America, Inc, p. 218 1 p.Research output: Chapter in Book/Report/Conference proceeding › Conference abstract in proceedings › 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 measurement of wrist synovitis from contrast-enhanced MRI: a registration-centered approach
Mysling, P., Darkner, Sune, Sporring, Jon, Dam, E. & Lillholm, Martin, 2013, Medical Imaging 2013: Image Processing. Ourselin, S. & Haynor, D. R. (eds.). SPIE - International Society for Optical Engineering, 6 p. 86692U. (Progress in Biomedical Optics and Imaging; No. 36, Vol. 14).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › 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
- Published
Automatic segmentation of high-and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative
Dam, Erik Bjørnager, Lillholm, Martin, Marques, J. & Nielsen, Mads, 2015, In: SPIE Journal of Medical Imaging. 2, 2, 13 p., 024001.Research output: Contribution to journal › Journal article › Research › peer-review
- Published
Automatic segmentation of vertebrae from radiographs: a sample-driven active shape model approach
Mysling, P., Petersen, P. K., Nielsen, Mads & Lillholm, Martin, 2011, Machine Learning in Medical Imaging: Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011. Proceedings. Suzuki, K., Wang, F., Shen, D. & Yan, P. (eds.). Springer, p. 10-17 8 p. (Lecture notes in computer science, Vol. 7009).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Basic image features (BIFs) arising from approximate symmetry type
Griffin, L. D., Lillholm, Martin, Crosier, M. & van Sande, J., 2009, Scale Space and Variational Methods in Computer Vision: Second International Conference, SSVM 2009, Voss, Norway, June 1-5, 2009. Proceedings. Tai, X-C., Mørken, K., Lysaker, M. & Lie, K-A. (eds.). Springer, p. 343-355 13 p. (Lecture notes in computer science, Vol. 5567).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › 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
Breast tissue segmentation and mammographic risk scoring using deep learning
Petersen, P. K., Nielsen, Mads, Diao, P., Karssemeijer, N. & Lillholm, Martin, 2014, Breast imaging: 12th International Workshop, IWDM 2014, Gifu City, Japan, June 29 – July 2, 2014. Proceedings. Fujita, H., Hara, T. & Muramatsu, C. (eds.). Springer Science+Business Media, p. 88-94 7 p. (Lecture notes in computer science, Vol. 8539).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Brownian Images: a generic background model
Steenstrup Pedersen, Kim & Lillholm, Martin, 2004, Proceedings of the ECCV'04 Workshop on Statistical Learning in Computer Vision.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
- Published
Change in mammographic density across birth cohorts of Dutch breast cancer screening participants
Napolitano, George, Lynge, Elsebeth, Lillholm, Martin, Vejborg, I. M. M., van Gils, C. H., Nielsen, Mads & Karssemeijer, N., 2019, In: International Journal of Cancer. 145, 11, p. 2954-2962 9 p.Research output: Contribution to journal › Journal article › Research › peer-review
Classifying local image symmetry using a co-localised family of linear filters
Griffin, L. D. & Lillholm, Martin, 2008, In: Perception. 37, Supplement, p. 122-122 1 p.Research output: Contribution to journal › Conference abstract in journal › Research
- Published
Computer analysis method for analyzing images involves applying algorithm to aligned images to extract quantitative estimate of difference in volume of object shown in second image by calculating change in volume of object
Pai, A. S. U., Sørensen, L., Dam, E., Lillholm, Martin & Nielsen, Mads, 2014, IPC No. A61B-005/00, Patent No. US2014357978-A1, 4 Dec 2014, Priority date 4 Jun 2013, Priority No. US909666Research output: Patent
- Published
Computer based method for determining the size of an objects in an image
Pai, A. S. U., Sørensen, L., Dam, E. B., Lillholm, Martin & Nielsen, Mads, 4 Dec 2014, Priority date 4 Dec 2014Research output: Patent
- Published
Cube propagation for focal brain atrophy estimation
Pai, A. S. U., Sørensen, L., Darkner, Sune, Mysling, P., Jørgensen, D. R., Dam, E. B., Lillholm, Martin, Oh, J., Chen, G., Suhy, J., Sporring, Jon & Nielsen, Mads, 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging. IEEE, p. 402-405 4 p.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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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627
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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