Mads Nielsen

Mads Nielsen

Professor

ORCID: 0000-0003-1535-068X

Publication year:
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11 - 20 out of 366Page size: 10
  1. 2023
  2. Published

    Deep Learning-Based Assessment of Cerebral Microbleeds in COVID-19

    Ferrer, N. R., Vendela Sagar, M., Klein, Kiril Vadimovic, Kruuse, Christina Rostrup, Nielsen, Mads & Mehdipour Ghazi, Mostafa, 2023, 2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023. IEEE Computer Society Press, p. 1-4 4 p. (Proceedings - International Symposium on Biomedical Imaging, Vol. 2023-April).

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

  3. Published

    Diagnostic test accuracy study of a commercially available deep learning algorithm for ischemic lesion detection on brain MRIs in suspected stroke patients from a non-comprehensive stroke center

    Krag, C. H., Müller, F. C., Gandrup, K. L., Raaschou, H., Andersen, Michael Brun, Brejnebøl, M. W., Sagar, M. V., Bojsen, J. A., Rasmussen, B. S., Graumann, O., Nielsen, Mads, Kruuse, Christina Rostrup & Boesen, Mikael Ploug, 2023, In: European Journal of Radiology. 168, 8 p., 111126.

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

  4. Published

    Instance-Specific Augmentation of Brain MRIs with Variational Autoencoders

    Middleton, Jon Anthony, Bauer, Marko, Johansen, Jacob, Nielsen, Mads, Sommer, Stefan Horst & Pai, A. S. U., 2023, Medical Applications with Disentanglements : First MICCAI Workshop, MAD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. Springer, p. 49-58 (Lecture Notes in Computer Science, Vol. 13823).

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

  5. Published

    Partial feedback online transfer learning with multi-source domains

    Kang, Zhongfeng, Nielsen, Mads, Yang, B. & Mehdipour Ghazi, Mostafa, 2023, In: Information Fusion. 89, p. 29-40

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

  6. Published

    Robust cross-vendor mammographic texture models using augmentation-based domain adaptation for long-term breast cancer risk

    Lauritzen, Andreas, von Euler-Chelpin, My Catarina, Lynge, Elsebeth, Vejborg, I., Nielsen, Mads, Karssemeijer, N. & Lillholm, Martin, 2023, In: Journal of Medical Imaging. 10, 5, p. 1-16 054003.

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

  7. 2022
  8. Published

    Multimodal Variational Autoencoders for Semi-Supervised Learning: In Defense of Product-of-Experts

    Kutuzova, S., Krause, Oswin, McCloskey, D., Nielsen, Mads & Igel, Christian, Jan 2022, arXiv.org, 29 p.

    Research output: Working paper › Preprint › Research

  9. Published

    A buffered online transfer learning algorithm with multi-layer network

    Kang, Zhongfeng, Yang, B., Nielsen, Mads, Deng, L. & Yang, S., 2022, In: Neurocomputing. 488, p. 581-597 17 p.

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

  10. Published

    An Artificial Intelligence–based Mammography Screening Protocol for Breast Cancer: Outcome and Radiologist Workload

    Lauritzen, Andreas, Rodríguez-Ruiz, A., von Euler-Chelpin, My Catarina, Lynge, Elsebeth, Vejborg, I., Nielsen, Mads, Karssemeijer, N. & Lillholm, Martin, 2022, In: Radiology. 304, 1, p. 41-49

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

  11. Published

    Augmentation based unsupervised domain adaptation

    Orbes-Arteaga, M., Varsavsky, T., Sørensen, L., Nielsen, Mads, Pai, A. S. U., Ourselin, S., Modat , M. & Cardoso, M. J., 2022, arXiv.org, 12 p.

    Research output: Working paper › Preprint › Research

  12. Published

    FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain

    Mehdipour Ghazi, Mostafa & Nielsen, Mads, 2022, arXiv.org, 18 p.

    Research output: Working paper › Preprint › Research

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