1. 2019
  2. Learning an MR acquisition-invariant representation using Siamese neural networks

    Kouw, W. M., Loog, M., Bartels, L. W. & Mendrik, A. M., 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) : Proceedings. Danvers: IEEE, p. 364-367 4 p.

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

  3. On the Statistical Detection of Adversarial Instances over Encrypted Data

    Sheikhalishahi, M., Nateghizad, M., Martinelli, F., Erkin, Z. & Loog, M., 2019, Security and Trust Management - 15th International Workshop, STM 2019, Proceedings. Mauw, S. & Conti, M. (eds.). Springer, p. 71-88 18 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11738 LNCS).

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-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. Robust Importance-Weighted Cross-Validation under Sample Selection Bias

    Kouw, W. M., Krijthe, J. H. & Loog, M., 2019, 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP). Piscataway: IEEE, p. 1-6 6 p. 8918731

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

  6. 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

  7. 2018
  8. A benchmark and comparison of active learning for logistic regression

    Yang, Y. & Loog, M., 2018, In : Pattern Recognition. 83, p. 401-415 15 p.

    Research output: Contribution to journalArticleScientificpeer-review

  9. A spatio-temporal reference model of the aging brain

    huizinga, W., Poot, D., Vernooij, M. W., Roshchupkin, G. V., bron, E. E., Ikram, M. A., Rueckert, D., Niessen, W. & Klein, S., 2018, In : NeuroImage. 169, p. 11-22

    Research output: Contribution to journalArticleScientificpeer-review

  10. A variance maximization criterion for active learning

    Yang, Y. & Loog, M., 2018, In : Pattern Recognition. 78, p. 358-370 13 p.

    Research output: Contribution to journalArticleScientificpeer-review

  11. Asymmetric kernel in Gaussian Processes for learning target variance

    Pintea, S. L., van Gemert, J. C. & Smeulders, A. W. M., 2018, In : Pattern Recognition Letters. 108, p. 70-77 8 p.

    Research output: Contribution to journalArticleScientificpeer-review

  12. Contextual loss functions for optimization of convolutional neural networks generating pseudo CTs from MRI

    van Stralen, M., Zhou, Y., Wozny, P. J., Seevinck, P. R. & Loog, M., 2018, Medical Imaging 2018: Image Processing. Angelini, E. D. & Landman, B. A. (eds.). Bellingham: SPIE, p. 105741N-1 - 105741N-6 6 p. 105741N. (Proceedings of Spie; vol. 10574).

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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