Publikationen

2025

• First Evaluation of the IEA Wind Task 52 LAC Summer Games 2024

  Schlipf, D. . (2025). First Evaluation of the IEA Wind Task 52 LAC Summer Games 2024. Zenodo. http://doi.org/10.5281/zenodo.14778978 (Original work published 2025)

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    • Schlipf, David

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    • Fachbereich 2: Energy and Life Science

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  • 2025
  • Miscellaneous
  • • David Schlipf

• "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

• Automation Bias in AI-assisted Medical Decision-making under Time Pressure in Computational Pathology

  Rosbach, E. ., Ganz, J. ., Ammeling, J. ., Riener, A. ., & Aubreville, M. . (2025). Automation Bias in AI-assisted Medical Decision-making under Time Pressure in Computational Pathology. Bildverarbeitung für die Medizin 2025. Wiesbaden: Springer Fachmedien Wiesbaden. (Original work published 2025)

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  Artificial intelligence (AI)-based clinical decision support systems (CDSS) promise to enhance diagnostic accuracy and efficiency in computational pathology. However, human-AI collaboration might introduce automation bias, where users uncritically follow automated cues. This bias may worsen when time pressure strains practitioners’ cognitive resources. We quantified automation bias by measuring the adoption of negative system consultations and examined the role of time pressure in a web-based experiment, where trained pathology experts (n=28) estimated tumor cell percentages. Our results indicate that while AI integration led to a statistically significant increase in overall performance, it also resulted in a 7% automation bias rate, where initially correct evaluations were overturned by erroneous AI advice. Conversely, time pressure did not exacerbate automation bias occurrence, but appeared to increase its severity, evidenced by heightened reliance on the system’s negative consultations and subsequent performance decline. These findings highlight potential risks of AI use in healthcare. The final dataset (including a table with image patch details), participant demographics, and source code are available at: https://github.com/emelyrosbach/AB-TP.git

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    • Aubreville, Marc

  • Abteilung/Einrichtung

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

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    • Gesundheit und Life Sciences

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

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    • Fachbereich 3: Information und Kommunikation

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  • 2025
  • Conference Proceedings
  • • Emely Rosbach
    • Jonathan Ganz
    • Jonas Ammeling
    • Andreas Riener
    • Marc Aubreville

• 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

• On the road to interactive LLM-based systematic mapping studies

  Petersen, K. ., & Gerken, J. M. (2025). On the road to interactive LLM-based systematic mapping studies. Information and Software Technology, 178. (Original work published Februar 2025)

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    • Petersen, Kai
    • Gerken, Jan

  • Abteilung/Einrichtung

    • FLAIR - Flensburg Artificial Intelligence Research

  • Fachbereich

    • Fachbereich 4: Wirtschaft

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  • 2025
  • Journal Article
  • • Kai Petersen
    • Jan Gerken
  • • Large language models
    • Systematic mapping studies
    • GPT

• Joint Frequency Stabilisation in Future 100% Renewable Electric Power Systems

  Reese, L. ., Rettig, A. ., Jauch, C. ., Domin, R. J., & Karshüning, T. . (2025). Joint Frequency Stabilisation in Future 100% Renewable Electric Power Systems. Energies, 18, 17. http://doi.org/10.3390/en18020418 (Original work published Januar 2025)

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  Due to the energy transition, the future electric power system will face further challenges that affect the functionality of the electricity grid and therefore the security of supply. For this reason, this article examines the future frequency stabilisation in a 100% renewable electric power system. A focus is set on the provision of inertia and frequency containment reserve. Today, the frequency stabilisation in most power systems is based on synchronous generators. By using grid-forming frequency converters, a large potential of alternative frequency stabilisation reserves can be tapped. Consequently, frequency stabilisation is not a problem of existing capacities but whether and how these are utilised. Therefore, in this paper, a collaborative approach to realise frequency stabilisation is proposed. By distributing the required inertia and frequency containment reserve across all technologies that are able to provide it, the relative contribution of each individual provider is low. To cover the need for frequency containment reserve, each capable technology would have to provide less than 1% of its rated power. The inertia demand can be covered by the available capacities at a coverage ratio of 171% (excluding wind power) to 217% (all capacities). As a result, it is proposed that provision of frequency stabilisation is made mandatory for all capable technologies. The joint provision distributes the burden of frequency stabilisation across many participants and hence increases redundancy. It ensures the stability of future electricity grids, and at the same time, it reduces the technological and economic effort. The findings are presented for the example of the German electricity grid.

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    • Jauch, Clemens

  • Forschungsprojekt

    • Inno!Nord: Innovationslabor: Speicher zur Nutzung erneuerbarer Energien im echten Norden

  • Abteilung/Einrichtung

    • Wind Energy Technology Institute

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    • Erneuerbare Energien und nachhaltige Entwicklung

  • Fachbereich

    • Fachbereich 2: Energy and Life Science

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  • 2025
  • Journal Article
  • • Lisanne Reese
    • Arne Rettig
    • Prof. Dr. Clemens Jauch
    • Richard Domin
    • Tom Karshüning
  • • electric power system
    • energy transition
    • frequency containment reserve
    • frequency stabilisation
    • inertia
    • renewable energy
    • synchronous stability

• 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

• 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

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    • 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

• Investigation of Class Separability within Object Detection Models in Histopathology

  Ammeling, J. ., Ganz, J. ., Wilm, F. ., Breininger, K. ., & Aubreville, M. . (2025). Investigation of Class Separability within Object Detection Models in Histopathology. IEEE Transactions on Medical Imaging, 1–1. http://doi.org/10.1109/TMI.2025.3560134

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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

  • Profilbereich

    • Informatik und Gestaltung

  • Forschungsfeld

    • Data Science und Künstliche Intelligenz

  • Fachbereich

    • Fachbereich 3: Information und Kommunikation

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  • 2025
  • Journal Article
  • • Jonas Ammeling
    • Jonathan Ganz
    • Frauke Wilm
    • Katharina Breininger
    • Marc Aubreville
  • • Feature extraction
    • Object detection
    • Measurement
    • Adaptation models
    • histopathology
    • Training
    • Complexity theory
    • YOLO
    • Location awareness
    • Data mining
    • object detection
    • latent space discrimination

• 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