Martin Lillholm
Professor
Machine Learning
Universitetsparken 1
2100 København Ø
ORCID: 0000-0002-1402-6899
1 - 2 ud af 2Pr. side: 10
- 2014
- Udgivet
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. (red.). Springer Science+Business Media, s. 88-94 7 s. (Lecture notes in computer science, Bind 8539).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
- Udgivet
Dementia diagnosis using MRI cortical thickness, shape, texture, and volumetry
Sørensen, L., Pai, A. S. U., Anker, C., Balas, I., Lillholm, Martin, Igel, Christian & Nielsen, Mads, 2014, MICCAI 2014 Workshop Proceedings: Challenge on Computer-Aided Diagnosis of Dementia Based on Structural MRI Data. Bron, E. E., Smits, M., van Swieten, J. C., Niessen, W. J. & Klein, S. (red.). Erasmus Universiteit Rotterdam, s. 111-118 8 s.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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Automatic segmentation of high-and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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