1. 2019
  2. On Social Involvement in Mingling Scenarios: Detecting Associates of F-formations in Still Images

    Zhang, L. & Hung, H., 2019, (Accepted/In press) In : IEEE Transactions on Affective Computing. p. 1-13 13 p.

    Research output: Contribution to journalArticleScientificpeer-review

  3. PD-L1 blockade engages tumor-infiltrating lymphocytes to co-express targetable activating and inhibitory receptors

    Beyrend, G., van der Gracht, E., Yilmaz, A., van Duikeren, S., Camps, M., Hollt, T., Vilanova, A., van Unen, V., Koning, F., de Miranda, N. F. C. C., Arens, R. & Ossendorp, F., 2019, In : Journal for ImmunoTherapy of Cancer. 7, 1, p. 1-14 14 p., 217.

    Research output: Contribution to journalArticleScientificpeer-review

  4. PRECISE: A domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors

    Mourragui, S., Loog, M., van der Wiel, M. A., Reinders, M. & Wessels, L., 2019, In : Bioinformatics. 35, 14, p. i510-i519 10 p., btz372.

    Research output: Contribution to journalArticleScientificpeer-review

  5. Quantification of aortic pulse wave velocity from a population based cohort: A fully automatic method

    Shahzad, R., Shankar, A., Amier, R., Nijveldt, R., Westenberg, J. J. M., de Roos, A., Lelieveldt, B. P. F. & van Der Geest, R. J., 2019, In : Journal of Cardiovascular Magnetic Resonance. 21, 1, p. 1-14 14 p., 27.

    Research output: Contribution to journalArticleScientificpeer-review

  6. Quantitative error prediction of medical image registration using regression forests

    Sokooti, H., Saygili, G., Glocker, B., Lelieveldt, B. & Staring, M., 2019, In : Medical Image Analysis. 56, p. 110-121 12 p.

    Research output: Contribution to journalArticleScientificpeer-review

  7. Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer

    Elmahdy, M. S., Jagt, T., Zinkstok, R. T., Qiao, Y., Shahzad, R., Sokooti, H., Yousefi, S., Incrocci, L., Marijnen, C. A. M., Hoogeman, M. & Staring, M., 2019, In : Medical Physics. 46, 8, p. 3329-3343 15 p.

    Research output: Contribution to journalArticleScientificpeer-review

  8. Single shot active learning using pseudo annotators

    Yang, Y. & Loog, M., 2019, In : Pattern Recognition. 89, p. 22-31 10 p.

    Research output: Contribution to journalArticleScientificpeer-review

  9. The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning

    Krijthe, J. & Loog, M., 2019, (Accepted/In press) Advances in Neural Information Processing Systems. p. 1793-1802 10 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientific

  10. The representation of speech in deep neural networks

    Scharenborg, O., van der Gouw, N., Larson, M. & Marchiori, E., 2019, MultiMedia Modeling: 25th International Conference, MMM 2019, Proceedings. Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, W-H. & Vrochidis, S. (eds.). Part II ed. Cham: Springer Verlag, p. 194-205 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11296 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  11. Using phase instead of optical flow for action recognition

    Hommos, O., Pintea, S. L., Mettes, P. S. M. & van Gemert, J. C., 2019, Computer Vision – ECCV 2018 Workshops, Proceedings. Leal-Taixé, L. & Roth, S. (eds.). Springer Verlag, p. 678-691 14 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11134 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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