Department of Computer Science

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  1. Published

    A transfer-learning approach to image segmentation across scanners by maximizing distribution similarity

    van Opbroek, A., Ikram, M. A., Vernooij, M. W. & de Bruijne, Marleen, 2013, Machine Learning in Medical Imaging: 4th International Workshop, MLMI 2013, held in conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013. Proceedings. Wu, G., Zhang, D., Shen, D., Yan, P., Suzuki, K. & Wang, F. (eds.). Springer, p. 49-56 8 p. (Lecture notes in computer science, Vol. 8184).

    Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  2. Published

    Automated brain-tissue segmentation by multi-feature SVM classification

    van Opbroek, A., van der Lijn, F. & de Bruijne, Marleen, 2013, The MICCAI Grand Challenge on MR Brain Image Segmentation (MRBrainS13). 8 p. (The MIDAS Journal).

    Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research

  3. Published

    Feature-space transformation improves supervised segmentation across scanners

    van Opbroek, A., Achterberg, H. C. & de Bruijne, Marleen, 2015, Machine learning meets medical imaging: First International Workshop, MLMMI 2015, Held in Conjunction with ICML 2015, Lille, France, July 11, 2015, Revised Selected Papers. Springer, p. 85-93 9 p. (Lecture notes in computer science, Vol. 9487).

    Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  4. Published

    Weighting training images by maximizing distribution similarity for supervised segmentation across scanners

    van Opbroek, A., Vernooij, M. W., Ikram, M. A. & de Bruijne, Marleen, 2015, In: Medical Image Analysis. 24, 1, p. 245-254 10 p.

    Research output: Contribution to journal › Journal article › Research › peer-review

  5. Published

    Transfer learning improves supervised image segmentation across imaging protocols

    van Opbroek, A., Ikram, M. A., Vernooij, M. W. & de Bruijne, Marleen, 2015, In: IEEE Transactions on Medical Imaging. 34, 5, p. 1018-1030 13 p.

    Research output: Contribution to journal › Journal article › Research › peer-review

  6. Published

    Combining generative and discriminative representation learning for lung CT analysis with convolutional restricted Boltzmann machines

    van Tulder, G. & de Bruijne, Marleen, 2016, In: IEEE Transactions on Medical Imaging. 35, 5, p. 1262-1272 11 p.

    Research output: Contribution to journal › Journal article › Research › peer-review

  7. Published

    Learning features for tissue classification with the classification restricted Boltzmann machine

    van Tulder, G. & de Bruijne, Marleen, 2014, Medical Computer Vision: Algorithms for Big Data: International Workshop, MCV 2014, Held in Conjunction with MICCAI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers. Menze, B., Langs, G., Montillo, A., Kelm, M., Müller, H., Zhang, S., Cai, W. T. & Metaxas, D. (eds.). Springer, p. 47-58 12 p. (Lecture notes in computer science).

    Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  8. Published

    Why does synthesized data improve multi-sequence classification?

    van Tulder, G. & de Bruijne, Marleen, 2015, Medical image computing and computer assisted interventions - MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I. Springer, p. 531-538 8 p. (Lecture notes in computer science, Vol. 9349).

    Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  9. Published

    Sensitivity of screening mammography by density and texture: a cohort study from a population-based screening program in Denmark

    von Euler-Chelpin, My Catarina, Lillholm, Martin, Vejborg, I., Nielsen, Mads & Lynge, Elsebeth, 2019, In: Breast Cancer Research. 21, 1

    Research output: Contribution to journal › Journal article › Research › peer-review

  10. Published

    Popular Politics: Comparing Popular Stories across News Media in Election Seasons

    Ørmen, Jacob & Petersen, C., 1 Jun 2017.

    Research output: Contribution to conference › Conference abstract for conference › Research

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