Publications

S. Nayak, C. Schuler, D. Saha, & T. Baumann. (2022). A Deep Dive Into Neural Synchrony Evaluation for Audio-visual Translation. International Conference on Multimodal Interaction. International Conference on Multimodal Interaction (ICMI), Bengaluru India. https://doi.org/10.1145/3536221.3556621

A. Windbuhler, S. Okkesim, O. Christ, S. Mottaghi, S. Rastogi, M. Schmuker, T. Baumann, & U. G. Hofmann. (2022). Machine Learning Approaches to Classify Anatomical Regions in Rodent Brain from High Density Recordings. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland, United Kingdom. https://doi.org/10.1109/EMBC48229.2022.9871702

T. Baumann, & S. Ashutosh. (2021). Evaluating Heuristics for Audio-Visual Translation. Proceedings of the Conference on Computational Humanities Research 2021. http://ceur-ws.org/Vol-2989/short_paper46.pdf

N. Krüger, K. Fischer, P. Manoonpong, O. Palinko, L. Bodenhagen, T. Baumann, J. Kjærum, I. Rano, L. Naik, W. K. Juel, F. Haarslev, J. Ignasov, E. Marchetti, R. M. Langedijk, A. Kollakidou, K. C. Jeppesen, C. Heidtmann, & L. Dalgaard. (2021). The SMOOTH-Robot: A Modular, Interactive Service Robot. Frontiers in Robotics and AI, 8. https://doi.org/10.3389/frobt.2021.645639

S. Nayak, T. Baumann, S. Bhattacharya, A. Karakanta, M. Negri, & M. Turchi. (2020). See me Speaking? Differentiating on Whether Words are Spoken On Screen or Off to Optimize Machine Dubbing. Companion Publication of the 2020 International Conference on Multimodal Interaction. ICMI ’20: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION, Virtual Event Netherlands. https://doi.org/10.1145/3395035.3425640

T. Himaya, A. Sehr, T. Yoshida, & S. Hangai. (2020). Text/Language-Independent Unknown Speaker Rejection Method Using LSP Codes. IEEE 9th Global Conference on Consumer Electronics (GCCE). https://doi.org/10.1109/GCCE50665.2020.9291835

J. Reschke, & A. Sehr. (2017). Face Recognition with Machine Learning in OpenCV: Fusion of the results with the Localization Data of an Acoustic Camera for Speaker Identification. Applied Research Conference. http://arxiv.org/pdf/1707.00835

K. Kinoshita, M. Delcroix, S. Gannot, E. Habets, R. Haeb-Umbach, W. Kellermann, V. Leutnant, R. Maas, T. Nakatani, B. Raj, A. Sehr, & T. Yoshioka. (2016). A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research. EURASIP Journal on Advances in Signal Processing. https://doi.org/10.1186/s13634-016-0306-6

R. Maas, C. Huemmer, A. Sehr, & W. Kellermann. (2015). A Bayesian view on acoustic model-based techniques for robust speech recognition. EURASIP Journal on Advances in Signal Processing. https://doi.org/10.1186/s13634-015-0287-x

W. Mauerer, S. Klessinger, & S. Scherzinger. (2022). Beyond the Badge: Reproducibility Engineering as a Lifetime Skill. SEENG@International Conference on Software Engineering (ICSE). https://doi.org/10.1145/3528231.3528359

L. Grabinger, F. Hauser, & J. Mottok. (2022). Accessing the Presentation of Causal Graphs and an Application of Gestalt Principles with Eye Tracking. In Proceedings of the 29th IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2022). 29th IEEE, New York, NY, USA. https://doi.org/10.1109/SANER53432.2022.00153

W. Mauerer, M. Joblin, D. A. Tamburri, C. Paradis, R. Kazman, & Sven Apel. (2021). In Search of Socio-Technical Congruence: A Large-Scale Longitudinal Study. IEEE Transactions on Software Engineering. https://doi.org/10.1109/TSE.2021.3082074

F. Hauser, S. Schreistetter, R. Reuter, J. Mottok, H. Gruber, K. Holmqvist, & N. Schorr. (2020). Code reviews in C++: Preliminary results from an eye tracking study. Proceedings of the ACM Eye Tracking Research and Application. https://doi.org/10.1145/3379156.3391980

R. Ramsauer, L. Bulwahn, D. Lohmann, & W. Mauerer. (2020). The Sound of Silence: Mining Security Vulnerabilities from Secret Integration Channels in Open-Source Projects. Proceedings of the 12th Cloud Computing Security Workshop (CCSW ’20). https://doi.org/10.1145/3411495.3421360

W. Mauerer, & S. Scherzinger. (2020). Educating Future Software Architects in the Art and Science of Analysing Software Data. Proc. SEUH Workshop. http://ceur-ws.org/Vol-2531/paper10.pdf

R. Ramsauer, D. Lohmann, & W. Mauerer. (2019). The List is the Process: Reliable Pre-Integration Tracking of Commits on Mailing Lists. Proceedings of the 41st International Conference on Software Engineering (ICSE ’19). https://doi.org/10.1109/ICSE.2019.00088

I. Hutzler, F. Hauser, R. Reuter, J. Mottok, & H. Gruber. (2018). Will the Noun/Verb Analysis be Used to Generate Class Diagrams? An Eye Tracking Study. 11th annual International Conference of Education, Research and Innovation, Seville, Spain. https://doi.org/10.21125/iceri.2018.1103

F. Hauser, M. Reiß, M. Nivala, J. Mottok, & H. Gruber. (2017). Eye Tracking Applied: Visual Expertise in Code Reviews. International Conference on Education and New Learning Technologies, Barcelona, Spain. https://doi.org/10.21125/edulearn.2017.1084

M. Joblin, S. Apel, C. Hunsen, & W. Mauerer. (2017). Classifying Developers into Core and Peripheral: An Empirical Study on Count and Network Metrics. 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE). https://doi.org/10.1109/icse.2017.23

M. Nivala, F. Hauser, J. Mottok, & H. Gruber. (2016). Developing visual expertise in software engineering: An eye tracking study. Proceedings of the IEEE EDUCON 2016. IEEE EDUCON 2016, Abu Dhabi, UAE. https://doi.org/10.1109/EDUCON.2016.7474614

M. Joblin, S. Apel, & Wolfgang Mauerer. (2016). Evolutionary trends of developer coordination: a network approach. Empirical Software Engineering. https://doi.org/10.1007/s10664-016-9478-9

M. Joblin, W. Mauerer, S. Apel, J. Siegmund, & D. Riehle. (2015). From Developer Networks to Verified Communities: A Fine-Grained Approach. 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering. https://doi.org/10.1109/icse.2015.73

N. Weiß, E. Pozzobon, J. Mottok, & V. Matoušek. (2021). Automated Reverse Engineering of CAN Protocols. Neural Network World. https://doi.org/10.14311/NNW.2021.31.015

A. Rudolph, S. Voget, & J. Mottok. (2018). A consistent safety case argumentation for artificial intelligence in safety related automotive systems. ERTS 2018, Toulouse, France. https://hal.science/hal-02156048

T. Winker, S. Groppe, V. J. E. Uotila, Z. Yan, J. Lu, M. Franz, & W. Mauerer. (2023). Quantum Machine Learning: Foundation, New Techniques, and Opportunities for Database Research. Proceedings of ACM SIGMOD/PODS International Conference on Management of Data. https://doi.org/10.1145/3555041.3589404

W. Mauerer, & S. Scherzinger. (2022). 1-2-3 Reproducibility for Quantum Software Experiments. Q-SANER@IEEE International Conference on Software Analysis, Evolution and Reengineering. https://doi.org/10.1109/SANER53432.2022.00148

M. Franz, L. Wolf, M. Periyasamy, C. Ufrecht, D. D. Scherer, A. Plinge, C. Mutschler, & W. Mauerer. (2022). Uncovering Instabilities in Variational-Quantum Deep Q-Networks. Journal of The Franklin Institute. https://doi.org/10.1016/j.jfranklin.2022.08.021

M. Schönberger, M. Franz, S. Scherzinger, & Wolfgang Mauerer. (2022). Peel | Pile? Cross-Framework Portability of Quantum Software. QSA@IEEE International Conference on Software Architecture (ICSA). https://doi.org/10.1109/ICSA-C54293.2022.00039

A. Bayerstadler, G. Becquin, J. Binder, T. Botter, H. Ehm, T. Ehmer, M. Erdmann, Norbert Gaus, Philipp Harbach, Maximilian Hess, Johannes Klepsch, Martin Leib, Sebastian Luber, Andre Luckow, Maximilian Mansky, Wolfgang Mauerer, Florian Neukart, Christoph Niedermeier, Lilly Palackal, … Fabian Winter. (2021). Industry Quantum Computing Applications. EPJ Quantum Technology. https://doi.org/10.1140/epjqt/s40507-021-00114-x

J. Weindl, M. G. Ehrnsperger, A. Prasetiadi, & T. F. Eibert. (2022). Interdigital Resonators in Wideband Ridged-Waveguide Filters. Advances in Radio Science, 20. https://doi.org/10.5194/ars-20-29-2023

M. G. Ehrnsperger, M. Noll, U. Siart, & T. F. Eibert. (2021). Background and Clutter Removal Techniques for Ultra Short Range Radar. 78–81. https://doi.org/10.1109/EuRAD48048.2021.00031

M. G. Ehrnsperger, M. Noll, S. Punzet, U. Siart, & T. F. Eibert. (2021). Dynamic Eigenimage Based Background and Clutter Suppression for Ultra Short-Range Radar. Advances in Radio Science, 19, 71–77. https://doi.org/10.5194/ars-19-71-2021

M. G. Ehrnsperger, T. Brenner, U. Siart, & T. F. Eibert. (2020). Real-Time Gesture Recognition with Shallow Convolutional Neural Networks Employing an Ultra Low Cost Radar System. German Microwave Conference. https://ieeexplore.ieee.org/document/9080241

J. Kornprobst, T. J. Mittermaier, R. A. M. Mauermayer, G. F. Hamberger, M. G. Ehrnsperger, B. Lehmeyer, M. T. Ivrlac, U. Imberg, T. F. Eibert, & J. A. Nossek. (2020). Compact Uniform Circular Quarter-Wavelength Monopole Antenna Arrays with Wideband Decoupling and Matching Networks. IEEE Transactions on Antennas and Propagation, 69, 769–783. https://doi.org/10.1109/TAP.2020.3016422

M. G. Ehrnsperger, U. Siart, M. Moosbühler, E. Daporta, & T. F. Eibert. (2019). Signal Degradation through Sediments on Safety-Critical Radar Sensors. Advances in Radio Science, 17, 91–100. https://doi.org/10.5194/ars-17-91-2019

M. G. Ehrnsperger, H. L. Hoese, U. Siart, & T. F. Eibert. (2019). Performance Investigation of Machine Learning Algorithms for Simple Human Gesture Recognition employing an Ultra Low Cost Radar System. Kleinheubacher Tagung. https://ieeexplore.ieee.org/document/8890169

A. Gschossmann, S. Jobst, J. Mottok, & R. Bierl. (2019). A Measure of Confidence of Artificial Neural Network Classifiers. ARCS Workshop 2019; 32nd International Conference on Architecture of Computing Systems. ARCS Workshop 2019; 32nd International Conference on Architecture of Computing Systems. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8836211&isnumber=8836190

M. G. Ehrnsperger, T. Brenner, H. L. Hoese, U. Siart, & T. F. Eibert. (n.d.). Real-Time Gesture Detection Based on Machine Learning Classification of Continuous Wave Radar Signals. IEEE Sensors Journal, 21, 8310–8322. https://doi.org/10.1109/JSEN.2020.3045616

T. Rück, M. Müller, S. Jobst, S. Weigl, J. Pangerl, R. Bierl, & F. Matysik. (n.d.). Digital Twin of a photoacoustic trace gas sensor for monitoring methane in complex gas compositions. Sensors and Actuators B: Chemical. https://doi.org/10.1016/j.snb.2022.133119

A. Kaps, T. Lehrer, I. Lepenies, M. Wagner, & F. Duddeck. (2023). Multi-Fidelity Optimization of Metal Sheets Concerning Manufacturability in Deep-Drawing Processes. Structural and Multidisciplinary Optimization, 66.8. https://doi.org/10.1007/s00158-023-03631-8

T. Lehrer, P. Stocker, F. Duddeck, & M. Wagner. (2023). Comparison of Low- vs. High-Dimensional Machine Learning Approaches for Sheet Metal Drawability Assessment. Third International Conference on Computational Science and AI in Industry (CSAI). Third International Conference on Computational Science and AI in Industry (CSAI). https://doi.org/10.35096/othr/pub-6477

T. Lehrer, A. Kaps, I. Lepenies, F. Duddeck, & M. Wagner. (2023). 2S-ML: A Simulation-Based Classification and Regression Approach for Drawability Assessment in Deep Drawing. International Journal of Material Forming, 16.5. https://doi.org/10.1007/s12289-023-01770-3

F. Schmid, T. Wild, J. Schneidewind, T. Vogl, L. Schuhegger, & S. Galka. (2022). Simulation Based Approach for Reconfiguration and Ramp Up Scenario Analysis in Factory Planning. 2022 Winter Simulation Conference (WSC). 2022 Winter Simulation Conference (WSC), Singapore. https://doi.org/10.1109/WSC57314.2022.10015310

R. Wöstmann, P. Schlunder, F. Temme, R. Klinkenberg, J. Kimberger, A. Spichtinger, M. Goldhacker, & J. Deuse. (2020). Conception of a Reference Architecture for Machine Learning in the Process Industry. 2020 IEEE International Conference on Big Data (Big Data), 1726–1735. https://doi.org/10.1109/BigData50022.2020.9378290

M. Weiß. (2021). Optimization of Cartesian Tasks with Configuration Selection. 2nd IMA Conference on Mathematics of Robotics. https://doi.org/10.1007/978-3-030-91352-6_16

M. G. Weiß. (2019). Optimal Object Placement Using a Virtual Axis. International Symposium on Advances in Robot Kinematics. https://doi.org/10.1007/978-3-319-93188-3_14

M. G. Weiß. (2015). A Class of 6R Robots and Poses with 16 Analytical Solutions. Proceedings of the IMA Conference on Mathematics of Robotics. https://cdn.ima.org.uk/wp/wp-content/uploads/2017/12/Weiss-A-Class-of-6R-Robots-and-Poses-with-16-Analytical-Solutions.pdf

M. Kurze, M. G. Weiß, & M. Otter. (2006). Methods and Tools to Design and Test Robot Control Systems. 37th International Symposium on Robotics (ISR). https://www.researchgate.net/publication/224928821_Methods_and_Tools_to_Design_and_Test_Robot_Control_Systems

M. Melzer, J. Dunnweber, & T. Baumann. (2022). Towards Smart Home Data Interpretation Using Analogies to Natural Language Processing. 2022 IEEE International Conference on Smart Internet of Things (SmartIoT). 2022 IEEE International Conference on Smart Internet of Things (SmartIoT). https://doi.org/10.1109/SmartIoT55134.2022.00020

J. Reschke. (2021). Fahrerintentionserkennung zur lichtbasierten Kommunikation mit Fußgängern. Doctoral Thesis. https://publikationen.bibliothek.kit.edu/1000140184

J. Reschke, & M. Klaußner. (2021). Explicit and Implicit Communication for Automated Vehicles. Proceedings of the 14th International Symposium on Automotive Lighting (ISAL). https://www.utzverlag.de/assets/pdf/44953dbl.pdf

J. Reschke, S. Berlitz, & C. Neumann. (2020). Personalised neural networks for a driver intention prediction: communication as enabler for automated driving. Advanced Optical Technologies, 9(6). https://doi.org/https://doi.org/10.1515/aot-2020-0035

J. Reschke, T. Höß, B. Schleyer, S. Berlitz, & C. Neumann. (2019). How Vehicles Learn to Display Symbols to Pedestrians. Proceedings of the 13th International Symposium on Automotive Lighting (ISAL). https://www.utzverlag.de/catalog/book/44817

J. Reschke, S. Prösl, M. Hamm, & C. Neumann. (2018). Assistance System for Vehicle-Pedestrian-Interaction: Deep Learning and Driver Intention Prediction. SIA VISION. https://wiki.unece.org/download/attachments/75531441/AVSR-02-16e.pdf?api=v2

V. Dahmen, S. Weikl, & K. Bogenberger. (2024). Interpretable Machine Learning for Mode Choice Modeling on Tracking-Based Revealed Preference Data. Transportation Research Record: Journal of the Transportation Research Board. https://doi.org/10.1177/03611981241246973

M. Rostami-Shahrbabak, S. Weikl, T. Niels, & K. Bogenberger. (2023). Modeling Vehicle Flocking in Lane-Free Automated Traffic. Transportation Research Record: Journal of the Transportation Research Board. https://doi.org/10.1177/03611981231159405

C. Hardt, S. Weikl, Y. Zhang, K. Lippoldt, & K. Bogenberger. (2022). Empirical Analysis of Free-Floating Carsharing Systems in Munich and Berlin. Transportation Research Board 101st Annual Meeting. https://www.researchgate.net/publication/357900050_Empirical_Analysis_of_Free-Floating_Carsharing_Systems_in_Munich_and_Berlin_TRBAM-22-02706

M. Rostami-Shahrbabak, S. Weikl, M. Akbarzadeh, & K. Bogenberger. (2022). A Two-Layer Approach for Vehicular Flocking in Lane-Free Environment. 11th Triennal Symposium on Transportation Analysis (TRISTAN). https://tristan2022.org/proceedings

S. Haimerl, C. Tschernitz, T. Schiller, C. Weig, S. Galka, & U. Briem. (2022). Development of a Simulation Framework for Urban Ropeway Systems and Analysis of the Planned Ropeway Network in Regensburg, Germany. Proceedings of the Winter Simulation Conference. https://doi.org/https://dl.acm.org/doi/10.5555/3586210.3586326

T. Stadler, A. Sarkar, & J. Dünnweber. (2021). Bus Demand Forecasting for Rural Areas Using XGBoost and Random Forest Algorithm. Computer Information Systems and Industrial Management. https://doi.org/10.1007/978-3-030-84340-3_36

S. Weikl, & K. Bogenberger. (2019). Integrated Relocation Model for Free-Floating Carsharing Systems: Field Trial Results. Transportation Research Record 2563,. https://doi.org/10.3141/2536-03

S. Weikl, K. Bogenberger, & N. Geroliminis. (2016). Simulation Framework for Proactive Relocation Strategies in Free-Floating Carsharing Systems. Transportation Research Board 95th Annual Meeting. https://mediatum.ub.tum.de/node?id=1631662

S. Weikl. (2016). A Mesoscopic Relocation Model for Free-Floating Carsharing Systems. Doctoral Thesis. https://athene-forschung.unibw.de/doc/112725/112725.pdf

S. Weikl, & K. Bogenberger. (2015). A Practice-Ready Relocation Model for Free-Floating Carsharing Systems with Electric Vehicles – Mesoscopic Approach and Field Trial Results. Transportation Research Part C: Emerging Technologies. https://doi.org/10.1016/j.trc.2015.06.024

S. Schmöller, S. Weikl, J. Müller, & K. Bogenberger. (2015). Empirical Analysis of Free-Floating Carsharing Usage: The Munich and Berlin Case. Transportation Research Part C: Emerging Technologies. https://doi.org/10.1016/j.trc.2015.03.008

S. Schmöller, S. Weikl, J. Müller, & K. Bogenberger. (2014). Empirical Data Analysis of Free-Floating Carsharing System. Transportation Research Board 93rd Annual Meeting. https://trid.trb.org/View/1289361

S. Weikl, & K. Bogenberger. (n.d.). Relocation Strategies and Algorithms for Free-Floating Car Sharing Systems. IEEE Intelligent Transportation Systems Magazine, 5. https://doi.org/10.1109/MITS.2013.2267810

S. Weikl, & P. Mayer. (n.d.). Data-Driven Quality Assessment of Cycling Networks. Frontiers in Future Transportation. https://doi.org/10.3389/ffutr.2023.1127742

C. Palm. (2023). History, Core Concepts, and Role of AI in Clinical Medicine. In M. F. Byrne, N. Parsa, A. T. Greenhill, D. Chahal, O. Ahmad, & U. Bargci (Eds.), AI in Clinical Medicine: A Practical Guide for Healthcare Professionals. https://doi.org/10.1002/9781119790686.ch5

R. Mendel, T. Rueckert, D. Wilhelm, D. Rueckert, & C. Palm. (2024). Motion-Corrected Moving Average: Including Post-Hoc Temporal Information for Improved Video Segmentation. https://doi.org/10.48550/arXiv.2403.03120

T. Rueckert, M. Rieder, H. Feussner, D. Wilhelm, D. Rueckert, & C. Palm. (2024). Smoke Classification in Laparoscopic Cholecystectomy Videos Incorporating Spatio-temporal Information. In A. Maier, T. M. Deserno, H. Handels, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2024: Proceedings, German Workshop on Medical Image Computing, March 10-12, 2024, Erlangen (pp. 298–303). https://doi.org/10.1007/978-3-658-44037-4_78

L. A. Souza Jr., A. G.C. Pacheco, L. A. Passos, M. C. S. Santana, R. Mendel, A. Ebigbo, A. Probst, H. Messmann, C. Palm, & J. P. Papa. (2024). DeepCraftFuse: visual and deeply-learnable features work better together for esophageal cancer detection in patients with Barrett’s esophagus. Neural Computing and Applications. https://doi.org/10.1007/s00521-024-09615-z

M. Gutbrod, B. Geisler, D. Rauber, & C. Palm. (2024). Data Augmentation for Images of Chronic Foot Wounds. In A. Maier, T. M. Deserno, H. Handels, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2024: Proceedings, German Workshop on Medical Image Computing, March 10-12, 2024, Erlangen (pp. 261–266). https://doi.org/10.1007/978-3-658-44037-4_71

T. Rueckert, D. Rueckert, & C. Palm. (2024). Corrigendum to “Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art” [Comput. Biol. Med. 169 (2024) 107929]. Computers in Biology and Medicine. https://doi.org/10.1016/j.compbiomed.2024.108027

S. Hammer, D. W. Nunes, M. Hammer, F. Zeman, M. Akers, A. Götz, A. Balla, M. C. Doppler, C. Fellner, N. D. P. B. Silva, S. Thurn, N. Verloh, C. Stroszczynski, W. A. Wohlgemuth, C. Palm, & W. Uller. (2024). Deep learning-based differentiation of peripheral high-flow and low-flow vascular malformations in T2-weighted short tau inversion recovery MRI. Clinical Hemorheology and Microcirculation, 1–15. https://doi.org/10.3233/CH-232071

T. Rückert, D. Rückert, & C. Palm. (2024). Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art. Computers in Biology and Medicine, 169. https://doi.org/10.1016/j.compbiomed.2024.107929

J. Frikel, & P. Bauer. (2023). A Deep Learning Based ρ-Filtered Layergram Reconstruction Method for Computed Tomography. 21st International Conference of Numerical Analysis and Applied Mathematics.

M. W. Scheppach, R. Mendel, A. Probst, S. Nagl, M. Meinikheim, H. C. Yip, L. H. S. Lau, P. Y. W. Chiu, C. Palm, H. Messmann, & A. Ebigbo. (2023). Effekt eines Künstliche Intelligenz (KI) – Algorithmus auf die Gefäßdetektion bei third space Endoskopien. Zeitschrift Für Gastroenterologie, 61. https://doi.org/10.1055/s-0043-1771980

S. Zellmer, D. Rauber, A. Probst, T. Weber, S. Nagl, S., C. Römmele, E. Schnoy, C. Palm, H. Messmann, & A. Ebigbo. (2023). Verwendung künstlicher Intelligenz bei der Detektion der Papilla duodeni major. Zeitschrift Für Gastroenterologie, 61. https://doi.org/10.1055/s-0043-1772000

M. Meinikheim, R. Mendel, A. Probst, M. W. Scheppach, S. Nagl, E. Schnoy, C. Römmele, F. Prinz, J. Schlottmann, H. Messmann, C. Palm, & A. Ebigbo. (2023). Einfluss von Künstlicher Intelligenz auf die Performance von niedergelassenen Gastroenterolog:innen bei der Beurteilung von Barrett-Ösophagus. Zeitschrift Für Gastroenterologie, 61. https://doi.org/10.1055/s-0043-1771711

T. Rückert, M. Rieder, D. Rauber, M. Xiao, E. Humolli, H. Feussner, D. Wilhelm, & C. Palm. (2023). Augmenting instrument segmentation in video sequences of minimally invasive surgery by synthetic smoky frames. International Journal of Computer Assisted Radiology and Surgery, 18, S54–S56. https://doi.org/10.1007/s11548-023-02878-2

R. Mendel, D. Rauber, & C. Palm. (2023). Exploring the Effects of Contrastive Learning on Homogeneous Medical Image Data. Bildverarbeitung Für Die Medizin 2023: Proceedings, German Workshop on Medical Image Computing, July 2- 4, 2023, Braunschweig, 128–13. https://doi.org/10.1007/978-3-658-41657-7

M. W. Scheppach, D. Rauber, J. Stallhofer, A. Muzalyova, V. Otten, C. Manzeneder, T. Schwamberger, J. Wanzl, J. Schlottmann, V. Tadic, A. Probst, E. Schnoy, C. Römmele, C. Fleischmann, M. Meinikheim, S. Miller, B. Märkl, C. Palm, H. Messmann, & A. Ebigbo. (2023). Performance comparison of a deep learning algorithm with endoscopists in the detection of duodenal villous atrophy (VA). Endoscopy, 55. https://doi.org/10.1055/s-0043-1765421

M. W. Scheppach, R. Mendel, A. Probst, D. Rauber, T. Rueckert, M. Meinikheim, C. Palm, H. Messmann, & A. Ebigbo. (2023). Real-time detection and delineation of tissue during third-space endoscopy using artificial intelligence (AI). Endoscopy, 55, S53–S54. https://doi.org/10.1055/s-0043-1765128

M. Meinikheim, R. Mendel, A. Probst, M. W. Scheppach, E. Schnoy, S. Nagl, C. Römmele, F. Prinz, J. Schlottmann, D. Golger, C. Palm, H. Messmann, & A. Ebigbo. (2023). AI-assisted detection and characterization of early Barrett’s neoplasia: Results of an Interim analysis. Endoscopy, 55. https://doi.org/10.1055/s-0043-1765437

M. W. Scheppach, D. Rauber, J. Stallhofer, A. Muzalyova, V. Otten, C. Manzeneder, T. Schwamberger, J. Wanzl, J. Schlottmann, V. Tadic, A. Probst, E. Schnoy, & C. Palm. (2023). Detection of duodenal villous atrophy on endoscopic images using a deep learning algorithm. Gastrointestinal Endoscopy. https://doi.org/10.1016/j.gie.2023.01.006

R. Mendel, D. Rauber, L. A. de Souza Jr., J. P. Papa, & C. Palm. (2023). Error-Correcting Mean-Teacher: Corrections instead of consistency-targets applied to semi-supervised medical image segmentation. Computers in Biology and Medicine. https://doi.org/10.1016/j.compbiomed.2023.106585

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