Publikationen

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Badilla-Solórzano, J., Ihler, S., Gellrich, N. C., & Spalthoff, S. (2023). Improving instrument detection for a robotic scrub nurse using multi-view voting. International journal of computer assisted radiology and surgery, 18(11), 1961-1968. https://doi.org/10.1007/s11548-023-03002-0
Ihler, S., & Kuhnke, F. (2023). AUC margin loss for limited, imbalanced and noisy medical image diagnosis: a case study on CheXpert5000. Current Directions in Biomedical Engineering, 9(1), 658-661. https://doi.org/10.1515/cdbme-2023-1165
Badilla Solórzano, J., Spindeldreier, S., Ihler, S., Gellrich, N-C., & Spalthoff, S. (2022). Deep-learning-based instrument detection for intra-operative robotic assistance. International journal of computer assisted radiology and surgery, 17(9), 1685-1695. https://doi.org/10.1007/s11548-022-02715-y
Budde, L., Ihler, S., Spindeldreier, S., Lücking, T., Meyer, T., Zimmermann, W-H., & Bodenschatz, E. (2022). A Six Degree of Freedom Extrusion Bioprinter. Current Directions in Biomedical Engineering, 8(2), 137-140. https://doi.org/10.1515/cdbme-2022-1036
Kuhnke, F., Ihler, S., & Ostermann, J. (2021). Relative Pose Consistency for Semi-Supervised Head Pose Estimation. in V. Struc, & M. Ivanovska (Hrsg.), 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021) (S. 1-8). (Proceedings - 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2021). https://doi.org/10.1109/FG52635.2021.9666992
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. https://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. https://arxiv.org/abs/2007.04928
Ihler, S., Kuhnke, F. K., Laves, M-H. V., & Ortmaier, T. (2020). Self-Supervised Domain Adaptation for Patient-Specific, Real-Time Tissue Tracking. in A. L. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. A. Zuluaga, S. K. Zhou, D. Racoceanu, & L. Joskowicz (Hrsg.), International Conference on Medical Image Computing and Computer Assisted Intervention (S. 54-64). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 12263 LNCS). Springer, Cham. https://doi.org/10.1007/978-3-030-59716-0_6
Laves, M-H., Ihler, S., Kortmann, K-P., & Ortmaier, T. (2020). Calibration of Model Uncertainty for Dropout Variational Inference. https://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. https://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. https://openreview.net/forum?id=S1xTGVhE5N
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. https://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. https://doi.org/10.48550/arXiv.1908.00788
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. https://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. https://doi.org/10.48550/arXiv.1904.00790
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 [11078_43] (Optics InfoBase Conference Papers; Band Part F142-ECBO 2019). OSA - The Optical Society. https://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. https://doi.org/10.48550/arXiv.1908.00792
Laves, M-H., Ihler, S., Kortmann, K-P., & Ortmaier, T. (2019). Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference. https://doi.org/10.48550/arXiv.1909.13550
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) https://doi.org/10.31256/hsmr2018.39