Martin Lillholm
Professor
Machine Learning
Universitetsparken 1
2100 København Ø
On image reconstruction from multiscale top points
Kanters, F., Lillholm, Martin, Duits, R., Janssen, B., Platel, B., Florack, L. & ter Haar Romeny, B., 2005, Scale Space and PDE Methods in Computer Vision: 5th International Conference, Scale-Space 2005, Hofgeismar, Germany, April 7-9, 2005. Proceedings. Kimmel, R., Sochen, N. A. & Weickert, J. (eds.). Springer, p. 431-442 12 p. (Lecture notes in computer science, Vol. 3459).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
- Published
Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring
Kallenberg, M. G. J., Petersen, P. K., Nielsen, Mads, Ng, A. Y., Diao, P., Igel, Christian, Vachon, C. M., Holland, K., Winkel, R. R., Karssemeijer, N. & Lillholm, Martin, 2016, In: IEEE Transactions on Medical Imaging. 35, 5, p. 1322-1331 10 p.Research output: Contribution to journal › Journal article › Research › peer-review
- Published
Learning density independent texture features
Kallenberg, M. G. J., Nielsen, Mads, Holland, K., Karssemeijer, N., Igel, Christian & Lillholm, Martin, 2016, Breast Imaging: 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings. Tingberg, A., Lång, K. & Timberg, P. (eds.). Springer, p. 299-306 8 p. (Lecture notes in computer science, Vol. 9699).Research output: Chapter in Book/Report/Conference proceeding › Article 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
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
Finding discriminative regions that optimally separate healthy and osteoarthritis knees
Jørgensen, D. R., Lillholm, Martin & Dam, E. B., 2011, In: Osteoarthritis and Cartilage. 19, Supplement 1, p. S192 414.Research output: Contribution to journal › Conference abstract in journal › 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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Predicting knee cartilage loss using adaptive partitioning of cartilage thickness maps
Jørgensen, D. R., Dam, Erik Bjørnager & Lillholm, Martin, 2013, In: Computers in Biology and Medicine. 43, 8, p. 1045-1052 8 p.Research output: Contribution to journal › Journal article › Research › peer-review
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On subregional analysis of cartilage loss from knee MRI
Jørgensen, D. R., Lillholm, Martin, Genant, H. K. & Dam, Erik Bjørnager, 2013, In: Cartilage. 4, 2, p. 121-130 10 p.Research output: Contribution to journal › Journal article › Research › peer-review
- Published
Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients
Jimenez-Solem, E., Petersen, T. S., Hansen, C., Hansen, C., Lioma, C., Igel, C., Boomsma, W., Krause, O., Lorenzen, S., Selvan, R., Petersen, J., Nyeland, M. E., Ankarfeldt, M. Z., Virenfeldt, G. M., Winther-Jensen, M., Linneberg, A., Ghazi, M. M., Detlefsen, N., Lauritzen, A. D., Smith, A. G. & 15 others, , 2021, In: Scientific Reports. 11, 1, 12 p., 3246.Research output: Contribution to journal › Journal article › Research › peer-review
Hypotheses for image features, icons and textons
Griffin, L. D. & Lillholm, Martin, 2006, In: International Journal of Computer Vision. 70, 3, p. 213-230 18 p.Research output: Contribution to journal › Journal article › Research › peer-review
Feature category systems for 2nd order local image structure induced by natural image statistics and otherwise
Griffin, L. D. & Lillholm, Martin, 2007, Human Vision and Electronic Imaging XII . Rogowitz, B. E., Pappas, T. N. & Daly, S. J. (eds.). SPIE - International Society for Optical Engineering, 11 p. 649209. (Proceedings of S P I E - International Society for Optical Engineering, Vol. 6492).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Image features and the 1-D, 2nd order gaussian derivative jet
Griffin, L. D. & Lillholm, Martin, 2005, Scale Space and PDE Methods in Computer Vision: 5th International Conference, Scale-Space 2005, Hofgeismar, Germany, April 7-9, 2005. Proceedings. Kimmel, R., Sochen, N. A. & Weickert, J. (eds.). Springer, p. 26-37 12 p. (Lecture notes in computer science, Vol. 3459).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Scale Space Methods in Computer Vision: 4th International Conference, Scale-Space 2003, Isle of Skye, UK, June 10-12, 2003, Proceedings
Griffin, L. D. (ed.) & Lillholm, Martin (ed.), 2003, Springer. (Lecture notes in computer science, Vol. 2695).Research output: Book/Report › Book › Research › peer-review
Symmetry sensitivities of derivative-of-gaussian filters
Griffin, L. D. & Lillholm, Martin, 2010, In: I E E E Transactions on Pattern Analysis and Machine Intelligence. 32, 6, p. 1072-1083 12 p.Research output: Contribution to journal › Journal article › 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
Natural image profiles are most likely to be step edges
Griffin, L. D., Lillholm, Martin & Nielsen, Mads, 2004, In: Vision Research. 44, 4, p. 407-421 15 p.Research output: Contribution to journal › Journal article › Research › peer-review
Mode estimation using pessimistic scale space tracking
Griffin, L. D. & Lillholm, Martin, 2003, Scale Space Methods in Computer Vision: 4th International Conference, Scale Space 2003 Isle of Skye, UK, June 10–12, 2003 Proceedings. Griffin, L. D. & Lillholm, M. (eds.). Springer, p. 266-280 15 p. (Lecture notes in computer science, Vol. 2695).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › 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
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
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 No. G06K-009/00, Patent No. US2010232671-A1, 16 Sep 2010, Priority date 17 Dec 2008, Priority No. US203094PResearch output: Patent
Method for analyzing magnetic resonance imaging (MRI) image of bone to identify e.g. osteoarthritis, involves combining features of textural information within region of interest (ROI) to estimate level of disease
Dam, E. B., Granlund, R. L. & Lillholm, Martin, 2011, IPC No. G06T-007/00, Patent No. WO2011151242-A1, 8 Dec 2011, Priority date 1 Jun 2010, Priority No. GB009101Research output: Patent
- Published
Quaternions, interpolation and animation
Dam, E., Koch, M. & Lillholm, Martin, 1998, Datalogisk Institut, Københavns Universitet, 103 p. (DIKU teknisk rapport; No. 5, Vol. 98).Research output: Working paper › Research
- Published
Fully automatic cartilage morphometry for knee MRI from the OAI
Dam, E., Marques, J., Zaim, S., Fuerst, T., Genant, H., Lillholm, Martin & Nielsen, Mads, 2012. 1 p.Research output: Contribution to conference › Conference abstract for conference › 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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339
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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