Martin Lillholm
Professor
Machine Learning
Universitetsparken 1
2100 København Ø
What do features tell about images?
Nielsen, Mads & Lillholm, Martin, 2001, Scale-Space and Morphology in Computer Vision: Third International Conference, Scale-Space 2001 Vancouver, Canada, July 7–8, 2001 Proceedings. Kerckhove, M. (ed.). Springer, p. 39-50 12 p. (Lecture notes in computer science, Vol. 2106).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
- Published
Vertebral fracture risk (VFR) score for fracture prediction in postmenopausal Women
Lillholm, Martin, Ghosh, A., Pettersen, P. C., de Bruijne, Marleen, Dam, E. B., Karsdal, M. A., Christiansen, C., Genant, H. K. & Nielsen, Mads, 2011, In: Osteoporosis International. 22, 7, p. 2119-2128 10 p.Research output: Contribution to journal › Journal article › 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 › peer-review
- Published
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 No. G06T-007/00, Patent No. WO2010142595-A1, 16 Dec 2010, Priority date 11 Jun 2009, Priority No. US268370Research output: Patent
- Published
The combined effect of mammographic texture and density on breast cancer risk: a cohort study
Wanders, J. O. P., van Gils, C. H., Karssemeijer, N., Holland, K., Kallenberg, M., Peeters, P. H. M., Nielsen, Mads & Lillholm, Martin, 2018, In: Breast Cancer Research. 20, 10 p., 36.Research output: Contribution to journal › Journal article › 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 › peer-review
Statistics and category systems for the shape index descriptor of local 2nd order natural image structure
Lillholm, Martin & Griffin, L. D., 2009, In: Image and Vision Computing. 27, 6, p. 771-781 11 p.Research output: Contribution to journal › Journal article › peer-review
Single stroke gaze gestures
Mollenbach, E., Hansen, J. P., Lillholm, Martin & Gale, A., 2009, CHI '09 Extended Abstracts on Human Factors in Computing Systems. Association for Computing Machinery, p. 4555-4560 6 p.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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, p. 177-180 4 p.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
- Published
Shape-based assessment of vertebral fracture risk in postmenopausal women using discriminative shape alignment
Crimi, A., Loog, M., de Bruijne, Marleen, Nielsen, Mads & Lillholm, Martin, 2012, In: Academic Radiology. 19, 4, p. 446-454 9 p.Research output: Contribution to journal › Journal article › peer-review
ID: 152298477
Most downloads
-
1571
downloads
Mammographic texture resemblance generalizes as an independent risk factor for breast cancer
Research output: Contribution to journal › Journal article › peer-review
Published -
571
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 › peer-review
Published -
301
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 › peer-review
Published