Martin Lillholm

Martin Lillholm

Professor


  1. Udgivet

    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, I: IEEE Transactions on Medical Imaging. 35, 5, s. 1322-1331 10 s.

    Publikation: Bidrag til tidsskriftTidsskriftartikelfagfællebedømt

  2. Udgivet

    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. (red.). Springer, s. 299-306 8 s. (Lecture notes in computer science, Bind 9699).

    Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

  3. Udgivet

    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, I: SPIE Journal of Medical Imaging. 3, 1, 11 s., 014005.

    Publikation: Bidrag til tidsskriftTidsskriftartikelfagfællebedømt

  4. Udgivet

    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, I: B M C Cancer. 16, 12 s., 414.

    Publikation: Bidrag til tidsskriftTidsskriftartikelfagfællebedømt

ID: 152298477