Martin Lillholm
Professor
Machine Learning
Universitetsparken 1
2100 København Ø
ORCID: 0000-0002-1402-6899
1 - 5 ud af 5Pr. side: 25
- 2010
- Udgivet
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, I: Medical Image Analysis. 14, 3, s. 255-264 10 s.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
- Udgivet
Method for identifying region of interest (ROI) in human organ for performing e.g. knee cartilage quantification, involves calculating weight of feature of image in map for minimizing sample size needed to discriminate between groups
Dam, E. B., Nielsen, Mads, Qazi, A. A., Lillholm, Martin & Jørgensen, D. R., 2010, IPC nr. G06K-009/00, Patentnr. US2010232671-A1, 16 sep. 2010, Prioritetsdato 17 dec. 2008, Prioritetsnr. US203094PPublikation: Patent
Single gaze gestures
Møllenbach, E., Lillholm, Martin, Gail, A. & Hansen, J. P., 2010, Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications. Association for Computing Machinery, s. 177-180 4 s.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Symmetry sensitivities of derivative-of-gaussian filters
Griffin, L. D. & Lillholm, Martin, 2010, I: I E E E Transactions on Pattern Analysis and Machine Intelligence. 32, 6, s. 1072-1083 12 s.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
- Udgivet
Two dimensional shape representation manipulation method for improving general procrustes alignment process, involves relating probable relative depth of landmark in three dimensional shape of body part
Chernoff, K., Nielsen, Mads & Lillholm, Martin, 2010, IPC nr. G06T-007/00, Patentnr. WO2010142595-A1, 16 dec. 2010, Prioritetsdato 11 jun. 2009, Prioritetsnr. US268370Publikation: Patent
ID: 152298477
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Mammographic texture resemblance generalizes as an independent risk factor for breast cancer
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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
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Automatic segmentation of high-and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative
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