Kenny Erleben

Kenny Erleben

Professor


  1. 2022
  2. Published

    A Direct Geometry Processing Cartilage Generation Method Using Segmented Bone Models from Datasets with Poor Cartilage Visibility

    nsv780, nsv780, Nielsen, M. K., Tascon Vidarte, J. D., Darkner, Sune & Erleben, Kenny, 2022, Computational Biomechanics for Medicine: Towards Translation and Better Patient Outcomes. Springer, p. 155.169

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

  3. Published

    Auto-segmentation of Hip Joints Using MultiPlanar UNet with Transfer Learning

    Xu, Peidi, nsv780, nsv780, Gholamalizadeh, T., Nielsen, Michael Bachmann, Erleben, Kenny & Darkner, Sune, 2022, Medical Image Learning with Limited and Noisy Data: First International Workshop, MILLanD 2022 Held in Conjunction with MICCAI 2022 Singapore, September 22, 2022 Proceedings. Zamzmi, G., Antani, S., Rajaraman, S., Xue, Z., Bagci, U. & Linguraru, M. G. (eds.). Springer Science and Business Media Deutschland GmbH, p. 153-162 10 p. (Medical Image Learning with Limited and Noisy Data, Vol. 13559).

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

  4. Published

    Fast Vortex Particle Method for Fluid-Character Interaction

    Meldgaard, A., Darkner, Sune & Erleben, Kenny, 2022, Proceedings of Graphics Interface 2022: Montréal, Quebec, 16 - 19 May 2022. ACM Press, p. 84-91

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

  5. Published

    Reducing Annotation Need in Self-explanatory Models for Lung Nodule Diagnosis

    Lu, J., Yin, C., Krause, Oswin, Erleben, Kenny, Nielsen, Michael Bachmann & Darkner, Sune, 2022, Interpretability of Machine Intelligence in Medical Image Computing. Reyes, M., Abreu, PH. & Cardoso, J. (eds.). Springer, p. 33-43 (Lecture Notes in Computer Science, Vol. 13611).

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

ID: 6466