Publikationen

Laves, M-H., Ihler, S., Fast, J. F., Kahrs, L. A., & Ortmaier, T. (2021). Recalibration of Aleatoric and Epistemic Regression Uncertainty in Medical Imaging. The Journal of Machine Learning for Biomedical Imaging (MELBA), MIDL 2020(1), 1-26. arxiv.org/abs/2104.12376

Ihler, S., Laves, M-H., & Ortmaier, T. (2020). Patient-Specific Domain Adaptation for Fast Optical Flow Based on Teacher-Student Knowledge Transfer. arxiv.org/abs/2007.04928

Laves, M-H., Ihler, S., Kortmann, K-P., & Ortmaier, T. (2020). Calibration of Model Uncertainty for Dropout Variational Inference. arxiv.org/abs/2006.11584

Napier, J. W., Ihler, S., Laves, M. H., Zabic, M., Heisterkamp, A., & Neu, W. (2020). Design of a novel MEMS based laser scanning laryngoscope to combine high precision laser cuts with simultaneous MHz OCT and stereo camera feedback. in Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2020 [112130J] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Band 11213). SPIE. doi.org/10.1117/12.2550806

Ihler, S., Laves, M-H., & Ortmaier, T. (2019). Towards Manifold Learning of Image-Based Motion Models for Oscillating Vocal Folds. in International Conference on Medical Imaging with Deep Learning--Extended Abstract Track

Laves, M. H., Ihler, S., Kahrs, L. A., & Ortmaier, T. (2019). Deep-learning-based 2.5D flow field estimation for maximum intensity projections of 4D optical coherence tomography. in B. Fei, & C. A. Linte (Hrsg.), Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling [109510R] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Band 10951). SPIE. doi.org/10.1117/12.2512952

Laves, M-H., Ihler, S., & Ortmaier, T. (2019). Deformable Medical Image Registration Using a Randomly-Initialized CNN as Regularization Prior.

Laves, M. H., Ihler, S., Ortmaier, T., & Kahrs, L. A. (2019). Quantifying the uncertainty of deep learning-based computer-aided diagnosis for patient safety. Current Directions in Biomedical Engineering, 5(1), 223-226. doi.org/10.1515/cdbme-2019-0057

Laves, M-H., Ihler, S., Kahrs, L. A., & Ortmaier, T. (2019). Retinal OCT disease classification with variational autoencoder regularization.

Laves, M. H., Ihler, S., Kahrs, L. A., & Ortmaier, T. (2019). Semantic denoising autoencoders for retinal optical coherence tomography. in European Conference on Biomedical Optics, ECBO_2019 (Optics InfoBase Conference Papers; Band Part F142-ECBO 2019). OSA - The Optical Society. doi.org/10.1117/12.2526936

Laves, M-H., Ihler, S., & Ortmaier, T. (2019). Uncertainty Quantification in Computer-Aided Diagnosis: Make Your Model say "I don't know" for Ambiguous Cases.

Laves, M-H., Ihler, S., Kortmann, K-P., & Ortmaier, T. (2019). Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference. Beitrag in 4th workshop on Bayesian Deep Learning, Vancouver, Kanada.

Modes, V., Ihler, S., Ortmaier, T., Nabavi, A., & Kahrs, J. B. (2018). Towards Concentric Tube Robots for Microsurgery: First Results in Eye-to-hand Visual Servoing. in Proceedings of The Hamlyn Symposium on Medical Robotics 2018 (S. 77-78) doi.org/10.31256/hsmr2018.39