Martin Lillholm

Martin Lillholm


  1. 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 journalJournal articleResearchpeer-review

  2. Published

    Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening: a case-control study

    Winkel, R. R., von Euler-Chelpin, My Catarina, Nielsen, Mads, Petersen, P. K., Lillholm, Martin, Nielsen, Michael Bachmann, Lynge, Elsebeth, Uldall, W. Y. & Vejborg, I. M. M., 2016, In: B M C Cancer. 16, 12 p., 414.

    Research output: Contribution to journalJournal articleResearchpeer-review

  3. 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 proceedingArticle in proceedingsResearchpeer-review

  4. Published

    Deformation-based atrophy computation by surface propagation and its application to Alzheimer’s disease

    Pai, A. S. U., Sporring, Jon, Darkner, Sune, Dam, Erik Bjørnager, Lillholm, Martin, Jørgensen, D., Oh, J., Chen, G., Suhy, J., Sørensen, L. & Nielsen, Mads, 2016, In: SPIE Journal of Medical Imaging. 3, 1, 11 p., 014005.

    Research output: Contribution to journalJournal articleResearchpeer-review

ID: 152298477