1. 2019
  2. A dissimilarity-based multiple instance learning approach for protein remote homology detection

    Mensi, A., Bicego, M., Lovato, P., Loog, M. & Tax, D. M. J., 1 Dec 2019, In : Pattern Recognition Letters. 128, p. 231-236 6 p.

    Research output: Contribution to journalArticleScientificpeer-review

  3. Multi-scale convolutional neural network for pixel-wise reconstruction of Van Gogh’s drawings

    Zeng, Y., van der Lubbe, J. C. A. & Loog, M., 1 Oct 2019, In : Machine Vision and Applications. 30, 7-8, p. 1229-1241 13 p.

    Research output: Contribution to journalArticleScientificpeer-review

  4. Gaussian process variance reduction by location selection

    Bottarelli, L. & Loog, M., 2019, In : Pattern Recognition Letters. 125, p. 727-734 8 p.

    Research output: Contribution to journalArticleScientificpeer-review

  5. 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/Report/Conference proceedingConference contributionScientificpeer-review

  6. 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

  7. 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

  8. 2018
  9. 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

  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. 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/Report/Conference proceedingConference contributionScientificpeer-review

  12. Effects of sampling skewness of the importance-weighted risk estimator on model selection

    Kouw, W. & Loog, M., 2018, 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, p. 1468-1473 6 p.

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

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