Publikationen

2025

• "When Two Wrongs Don t Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology

  Rosbach, E., Ammeling, J., Krügel, S., Kießig, A., Fritz, A., Ganz, J., … Aubreville, M. (2025). "When Two Wrongs Don t Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (S. 1–18). Yokohama Japan: ACM. http://doi.org/10.1145/3706598.3713319 (Original work published 2025)

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    • Aubreville, Marc

  • Abteilung/Einrichtung

    • CIVU – Center for Interaction, Visualization and Usability
    • FLAIR - Flensburg Artificial Intelligence Research

  • Profilbereich

    • Gesundheit und Life Sciences

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2025
  • Conference Paper
  • • Emely Rosbach
    • Jonas Ammeling
    • Sebastian Krügel
    • Angelika Kießig
    • Alexis Fritz
    • Jonathan Ganz
    • Chloé Puget
    • Taryn Donovan
    • Andrea Klang
    • Maximilian Köller
    • Pompei Bolfa
    • Marco Tecilla
    • Daniela Denk
    • Matti Kiupel
    • Georgios Paraschou
    • Mun Kok
    • Alexander Haake
    • Ronald De Krijger
    • Andreas Sonnen
    • Tanit Kasantikul
    • Gerry Dorrestein
    • Rebecca Smedley
    • Nikolas Stathonikos
    • Matthias Uhl
    • Christof Bertram
    • Andreas Riener
    • Marc Aubreville

• GPU Virtualization and Remoting Service for AI Acceleration at the Edge

  Gaikwad, N., Lübben, R., & Kosta, S. (2025). GPU Virtualization and Remoting Service for AI Acceleration at the Edge. In Summer School on Cyber-Physical Systems and Internet-of-Things. (Original work published Juli 2025)

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  • Forschungsprojekt

    • KI-Anwendungszentrum (KIAZ)

  • Abteilung/Einrichtung

    • FLAIR - Flensburg Artificial Intelligence Research

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 4: Wirtschaft

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  • 2025
  • Conference Paper
  • • Nikhil Gaikwad
    • Ralf Lübben
    • Sokol Kosta

• An Experimental Wind Farm Emulator with a Dual Variable-Speed Drive Configuration

  Gruman, N., Moses, P., & Jauch, C. (2025). An Experimental Wind Farm Emulator with a Dual Variable-Speed Drive Configuration. In 2025 Intermountain Engineering, Technology and Computing (IETC) (S. 6). Orem, UT, USA: IEEE. http://doi.org/10.1109/IETC64455.2025.11039473 (Original work published Juni 2025)

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  The rise of wind power in bulk electric systems creates a growing need to model, understand, and characterize responses to various sources of disruption. Despite the numerous advantages of computer models, physical emulators have enormous benefits in characterizing dynamic magnetic electromechanical phenomena which are unexpected or difficult for models to process accurately. The development of an experimental apparatus for physical wind farm emulation is described. The control and operation of an induction motor-generator setup for mimicking wind generator dynamics are demonstrated. Recommendations for further application of this emulator are also given.

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  • Personenseiten

    • Jauch, Clemens

  • Abteilung/Einrichtung

    • Wind Energy Technology Institute

  • Profilbereich

    • Erneuerbare Energien und nachhaltige Entwicklung

  • Forschungsfeld

    • Wind
    • Netzintegration

  • Fachbereich

    • Fachbereich 2: Energy and Life Science

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  • 2025
  • Conference Paper
  • • Noah Gruman
    • Paul Moses
    • Prof. Dr. Clemens Jauch
  • • Emulator
    • grid connection
    • induction machine
    • inverter, renewables
    • wind energy

• Minimum CO2 Saving Requirement for the Hydraulic-Pneumatic Flywheel System in the Rotor of a Wind Turbine

  Goga, A., Jauch, C., Alhrshy, L., Gagel, A., & Kloft, P. (2025). Minimum CO2 Saving Requirement for the Hydraulic-Pneumatic Flywheel System in the Rotor of a Wind Turbine. In NEIS Conference 2024 (S. 184–191). Hamburg: VDE Verlag GmbH. http://doi.org/10.30420/566464025 (Original work published März 2025)

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  Researchers in the wind sector continuously aim to lower mechanical and structural loads on wind turbines to reduce the amount of material used in their components. One such innovative concept that is being investigated here is the integration of a flywheel system into rotor blades. In contrast to its intended aim of reducing the material usage and thereby CO2 emissions, the flywheel system in its implementation requires additional materials, which increases the carbon footprint considerably. Hence, to achieve a net CO2 saving, the CO2 expenditure of the additional components needs to be saved by the system as a minimum. The purpose of this work is to quantify the rise in CO2 emissions, caused by the installation of the flywheel system, relative to the original design. Thereby, the flywheel system’s minimal performance requirement for an exemplary wind turbine is defined.

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    • Goga, Abhinay
    • Jauch, Clemens
    • Alhrshy, Laurence

  • Forschungsprojekt

    • Ressourceneffizienz in der Windenergie

  • Abteilung/Einrichtung

    • Wind Energy Technology Institute

  • Profilbereich

    • Erneuerbare Energien und nachhaltige Entwicklung

  • Forschungsfeld

    • Wind

  • Fachbereich

    • Fachbereich 2: Energy and Life Science

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  • 2025
  • Conference Paper
  • • Abhinay Goga
    • Prof. Dr. Clemens Jauch
    • Laurence Alhrshy
    • Andreas Gagel
    • Peter Kloft
  • • Wind Turbine
    • Variable inertia flywheel
    • Increase In CO2 Emissions
    • Construction Materials

• Comprehensive Dataset of Coarse Tumor Annotations for The Cancer Genome Atlas Breast Invasive Carcinoma

  Banerjee, S. ", Bertram, C. A., Ammeling, J., Weiss, V., Conrad, T., Klopfleisch, R., … Aubreville, M. (2025). Comprehensive Dataset of Coarse Tumor Annotations for The Cancer Genome Atlas Breast Invasive Carcinoma. In Bildverarbeitung für die Medizin 2025 (S. 260–265). Wiesbaden: Springer Fachmedien Wiesbaden. http://doi.org/10.1007/978-3-658-47422-5_56

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  Automated tumor segmentation of histologic images is crucial for the development of computer-assisted diagnostic workflows aiming at accurate prognostication. We present a dataset of coarse annotations of over 1,000 highresolution breast tumor images from The cancer genome atlas breast invasive carcinoma (TCGA-BRCA) repository, each annotated with binary segmentation masks that delineate coarse tumor areas. Additionally, a subset of 20 images includes fine-grained annotations, providing precise delineation of tumor boundaries beyond the broader outlines used in coarse annotations. Initial evaluations using U-Net and DeepLabv3 models show promising segmentation performance. On a held-out, coarsely annotated test set, U-Net achieved an average intersection over union (IoU) score of 0.795, while DeepLabv3 scored 0.783. On the finely annotated subset of this test set, U-Net reached an average IoU of 0.746, with DeepLabv3 slightly outperforming at 0.765. The public availability of this dataset aims to support research in automated tumor analysis, advancing diagnostic workflows and thereby ultimately improving breast cancer management.

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    • Aubreville, Marc

  • Forschungsprojekt

    • Robust and Accurate Multi-Tumor, Multi-Species, Multi-Laboratory and Multi-Scanner Mitosis Detection with Large-Scale Datasets and Artificial Intelligence

  • Abteilung/Einrichtung

    • CIVU – Center for Interaction, Visualization and Usability
    • FLAIR - Flensburg Artificial Intelligence Research

  • Profilbereich

    • Gesundheit und Life Sciences

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2025
  • Conference Paper
  • • Sweta Banerjee
    • Christof Bertram
    • Jonas Ammeling
    • Viktoria Weiss
    • Thomas Conrad
    • Robert Klopfleisch
    • Christopher Kaltenecker
    • Katharina Breininger
    • Marc Aubreville

• Relatedness Technologies: An Online Compendium and Systematic Review

  Wenhart, C., Ringfort-Felner, R., Wallbaum, T., Amidi, M., Albers, R., & Hassenzahl, M. (2025). Relatedness Technologies: An Online Compendium and Systematic Review. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. New York, NY, USA: Association for Computing Machinery. http://doi.org/10.1145/3706598.3714260

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  Over the past decades, numerous concepts and prototypes for fostering emotional connections across distance (relatedness technologies) have been proposed. This has made it challenging for researchers and designers in Human-Computer Interaction (HCI) to maintain a comprehensive overview and effectively build on previous work. To address this, we conducted a systematic literature search (PRISMA) and collected 241 concepts and prototypes (2010-2024). We organized this corpus according to key aspects: (1) target population, (2) theoretical grounding, (3) design, (4) evaluation, and (5) ethics. Based on this, we developed the “COmpendium of RElatedness Technologies” (CORE), an open-access, searchable online database that provides researchers and practitioners with a reliable repository to inform future work. In addition, we present a systematic review of the corpus, revealing that despite its long tradition work on relatedness technologies remains characterized by limited theoretical grounding, lack of robust empirical evidence of effects, and insufficient attention to ethical considerations.

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    • Wallbaum, Torben

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  • 2025
  • Conference Paper
  • • Christiane Wenhart
    • Ronda Ringfort-Felner
    • Torben Wallbaum
    • Maryam Amidi
    • Ruben Albers
    • Marc Hassenzahl
  • • Connectedness
    • Database
    • Experience Design
    • Relatedness
    • Relatedness Technology
    • Systematic Review

• REACH: A Card-based Approach to Guide Research Engagement and Science Communication Methods

  Amidi, M., Horstmann, A. C., Mertineit, J. A., & Wallbaum, T. (2025). REACH: A Card-based Approach to Guide Research Engagement and Science Communication Methods. In Proceedings of the 2025 Mensch und Computer 2025 (S. 694–699).

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    • Wallbaum, Torben

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  • 2025
  • Conference Paper
  • • Maryam Amidi
    • Aike Horstmann
    • Jana Mertineit
    • Torben Wallbaum

• Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br)

  Bertram, C. A. ", Weiss, V., Donovan, T. A., Banerjee, S., Conrad, T., Ammeling, J., … Aubreville, M. (2025). Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br). In "Bildverarbeitung für die Medizin 2025" (S. 113–118). "Wiesbaden": "Springer Fachmedien Wiesbaden".

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  "Assessment of the density of mitotic figures (MFs) in histologic tumor sections is an important prognostic marker for many tumor types, including breast cancer. Recently, it has been reported in multiple works that the quantity of MFs with an atypical morphology (atypical MFs, AMFs) might be an independent prognostic criterion for breast cancer. AMFs are an indicator of mutations in the genes regulating the cell cycle and can lead to aberrant chromosome constitution (aneuploidy) of the tumor cells. To facilitate further research on this topic using pattern recognition, we present the first ever publicly available dataset of atypical and normal MFs (AMi-Br). For this, we utilized two of the most popular MF datasets (MIDOG 2021 and TUPAC) and subclassified all MFs using a three expert majority vote. Our final dataset consists of 3,720 MFs, split into 832 AMFs (22.4\%) and 2,888 normal MFs (77.6\%) across all 223 tumor cases in the combined set. We provide baseline classification experiments to investigate the consistency of the dataset, using a Monte Carlo cross-validation and different strategies to combat class imbalance.We found an averaged balanced accuracy of up to 0.806 when using a patch-level data set split, and up to 0.713 when using a patient-level split."

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    • Aubreville, Marc

  • Forschungsprojekt

    • Robust and Accurate Multi-Tumor, Multi-Species, Multi-Laboratory and Multi-Scanner Mitosis Detection with Large-Scale Datasets and Artificial Intelligence

  • Abteilung/Einrichtung

    • FLAIR - Flensburg Artificial Intelligence Research
    • CIVU – Center for Interaction, Visualization and Usability

  • Profilbereich

    • Gesundheit und Life Sciences

  • Forschungsfeld

    • Digitale Transformation
    • Human-Computer-Interaction
    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2025
  • Conference Paper
  • • Christof Bertram
    • Viktoria Weiss
    • Taryn Donovan
    • Sweta Banerjee
    • Thomas Conrad
    • Jonas Ammeling
    • Robert Klopfleisch
    • Christopher Kaltenecker
    • Marc Aubreville

• MeasureTip: Towards a Finger-Worn Measurement Device

  Olberding, S., & Wallbaum, T. (2025). MeasureTip: Towards a Finger-Worn Measurement Device. In Proceedings of the 2025 Mensch und Computer 2025 (S. 673–677).

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    • Wallbaum, Torben

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  • 2025
  • Conference Paper
  • • Simon Olberding
    • Torben Wallbaum

• Is Self-supervision Enough?

  Ganz, J. ", Ammeling, J., Rosbach, E., Lausser, L., Bertram, C. A., Breininger, K., & Aubreville, M. (2025). Is Self-supervision Enough? In "Bildverarbeitung für die Medizin 2025" (S. 63–68). "Wiesbaden": "Springer Fachmedien Wiesbaden".

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  "Foundation models (FMs), i.e., models trained on a vast amount of typically unlabeled data, have become popular and available recently for the domain of histopathology. The key idea is to extract semantically rich vectors from any input patch, allowing for the use of simple subsequent classification networks potentially reducing the required amounts of labeled data, and increasing domain robustness. In this work, we investigate to which degree this also holds for mitotic figure classification. Utilizing two popular public mitotic figure datasets, we compared linear probing of five publicly available FMs against models trained on ImageNet and a simple ResNet50 end-to-end-trained baseline. We found that the end-to-end-trained baseline outperformed all FM-based classifiers, regardless of the amount of data provided. Additionally, we did not observe the FM-based classifiers to be more robust against domain shifts, rendering both of the above assumptions incorrect."

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    • Aubreville, Marc

  • Forschungsprojekt

    • Robust and Accurate Multi-Tumor, Multi-Species, Multi-Laboratory and Multi-Scanner Mitosis Detection with Large-Scale Datasets and Artificial Intelligence

  • Abteilung/Einrichtung

    • CIVU – Center for Interaction, Visualization and Usability
    • FLAIR - Flensburg Artificial Intelligence Research

  • Profilbereich

    • Gesundheit und Life Sciences

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2025
  • Conference Paper
  • • Jonathan Ganz
    • Jonas Ammeling
    • Emely Rosbach
    • Ludwig Lausser
    • Christof Bertram
    • Katharina Breininger
    • Marc Aubreville