Publikationen

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".

Abstract

"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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- Personenseiten
  
  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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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".

Abstract

"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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Weiterführende Links
- Personenseiten
  
  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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Enhancing Drone Operation Efficiency and Operator Experience: Integrating Extended Reality and Adaptive Systems with Situation-Aware Models

Bendig, H. (2024). Enhancing Drone Operation Efficiency and Operator Experience: Integrating Extended Reality and Adaptive Systems with Situation-Aware Models. Mensch und Computer 2024. Karlsruhe, Germany: Gesellschaft für Informatik e.V. http://doi.org/10.18420/muc2024-mci-dc-178 (Original work published September 2024)

Abstract

This research project investigates how adaptive Augmented Reality (AR) applications can improve the efficiency of drone operations and the experience of operators. Drones, particularly Unmanned Aerial Vehicles (UAVs), are increasingly employed in search and rescue (SAR) missions due to their rapid deployment capabilities and the ability to access hazardous areas or viewpoints that are otherwise unreachable. Operating drones in these high-demanding scenarios requires operators to possess certain cognitive abilities to manage the UAV effectively. Our research aims to develop a situation-aware interaction model that considers data from the task, the UAVs, and the human operator, offering support during demanding missions or low abilities. This model will be integrated into an AR application designed to adjust to the current situation, thereby enhancing operational outcomes and user experience.
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- DOI Verweis
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- Personenseiten
  
  Bendig, Henner
- Abteilung/Einrichtung
  
  CIVU – Center for Interaction, Visualization and Usability
  
  UsabilityLab
- Profilbereich
  
  Informatik und Gestaltung
- Forschungsfeld
  
  Human-Computer-Interaction
  
  Data Science und Künstliche Intelligenz
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  Fachbereich 3: Information und Kommunikation
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Enhancing Drone Operation Efficiency and Operator Experience: Integrating Extended Reality and Adaptive Systems with Situation-Aware Models

Bendig, H. (2024). Enhancing Drone Operation Efficiency and Operator Experience: Integrating Extended Reality and Adaptive Systems with Situation-Aware Models. Mensch und Computer 2024. Karlsruhe, Germany: Gesellschaft für Informatik e.V. http://doi.org/10.18420/muc2024-mci-dc-178 (Original work published September 2024)

Abstract

This research project investigates how adaptive Augmented Reality (AR) applications can improve the efficiency of drone operations and the experience of operators. Drones, particularly Unmanned Aerial Vehicles (UAVs), are increasingly employed in search and rescue (SAR) missions due to their rapid deployment capabilities and the ability to access hazardous areas or viewpoints that are otherwise unreachable. Operating drones in these high-demanding scenarios requires operators to possess certain cognitive abilities to manage the UAV effectively. Our research aims to develop a situation-aware interaction model that considers data from the task, the UAVs, and the human operator, offering support during demanding missions or low abilities. This model will be integrated into an AR application designed to adjust to the current situation, thereby enhancing operational outcomes and user experience.
- Suche auf Google Scholar
- DOI Verweis
Exportformate
Weiterführende Links
- Personenseiten
  
  Bendig, Henner
- Abteilung/Einrichtung
  
  CIVU – Center for Interaction, Visualization and Usability
  
  UsabilityLab
- Profilbereich
  
  Informatik und Gestaltung
- Forschungsfeld
  
  Human-Computer-Interaction
  
  Data Science und Künstliche Intelligenz
- Fachbereich
  
  Fachbereich 3: Information und Kommunikation
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Enhancing Spatial Problem-Solving Through Data-Driven Methods: A Practical Approach Utilizing Immersive Technologies and Multimodal Physical Tracking

Bendig, H. (2024). Enhancing Spatial Problem-Solving Through Data-Driven Methods: A Practical Approach Utilizing Immersive Technologies and Multimodal Physical Tracking. In Companion Proceedings of the 29th International Conference on Intelligent User Interfaces (S. 150–152). Greenville, SC, USA: Association for Computing Machinery. http://doi.org/10.1145/3640544.3645244 (Original work published März 2024)

Abstract

This project aims to explore how data-driven methods can be used to enhance humans’ problem-solving skills in spatial tasks and how they can be integrated into immersive tutoring systems. The goal is to provide an enhanced, state-dependent approach that is also applicable outside the laboratory, within real-life scenarios. Digital tutoring and learning systems are typically expert systems and follow a predefined training routine. Ongoing research concentrates on adaptive methods within affective- and cognition-aware systems, yet there is comparatively less investigation into recognizing and adapting problem-solving strategies. Spatial skill have been shown to be especially important for STEM (Science, Technology, Engineering, Mathematics) disciplines in general. Due to their design, immersive systems are particularly suitable for training of spatial skills and are thus frequently utilized for this.
- Suche auf Google Scholar
- DOI Verweis
Exportformate
Weiterführende Links
- Personenseiten
  
  Bendig, Henner
- Abteilung/Einrichtung
  
  CIVU – Center for Interaction, Visualization and Usability
  
  UsabilityLab
- Profilbereich
  
  Informatik und Gestaltung
- Forschungsfeld
  
  Human-Computer-Interaction
  
  Data Science und Künstliche Intelligenz
- Fachbereich
  
  Fachbereich 3: Information und Kommunikation
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Enhancing Spatial Problem-Solving Through Data-Driven Methods: A Practical Approach Utilizing Immersive Technologies and Multimodal Physical Tracking

Bendig, H. (2024). Enhancing Spatial Problem-Solving Through Data-Driven Methods: A Practical Approach Utilizing Immersive Technologies and Multimodal Physical Tracking. In Companion Proceedings of the 29th International Conference on Intelligent User Interfaces (S. 150–152). Greenville, SC, USA: Association for Computing Machinery. http://doi.org/10.1145/3640544.3645244 (Original work published März 2024)

Abstract

This project aims to explore how data-driven methods can be used to enhance humans’ problem-solving skills in spatial tasks and how they can be integrated into immersive tutoring systems. The goal is to provide an enhanced, state-dependent approach that is also applicable outside the laboratory, within real-life scenarios. Digital tutoring and learning systems are typically expert systems and follow a predefined training routine. Ongoing research concentrates on adaptive methods within affective- and cognition-aware systems, yet there is comparatively less investigation into recognizing and adapting problem-solving strategies. Spatial skill have been shown to be especially important for STEM (Science, Technology, Engineering, Mathematics) disciplines in general. Due to their design, immersive systems are particularly suitable for training of spatial skills and are thus frequently utilized for this.
- Suche auf Google Scholar
- DOI Verweis
Exportformate
Weiterführende Links
- Personenseiten
  
  Bendig, Henner
- Abteilung/Einrichtung
  
  CIVU – Center for Interaction, Visualization and Usability
  
  UsabilityLab
- Profilbereich
  
  Informatik und Gestaltung
- Forschungsfeld
  
  Human-Computer-Interaction
  
  Data Science und Künstliche Intelligenz
- Fachbereich
  
  Fachbereich 3: Information und Kommunikation
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An Interdisciplinary Perspective on AI-Supported Decision Making in Medicine

Ammeling, J., Aubreville, M., Fritz, A., Kießig, A., Krügel, S., & Uhl, M. (2024). An Interdisciplinary Perspective on AI-Supported Decision Making in Medicine. Technology in Society, 102791.
- Suche auf Google Scholar
Exportformate
Weiterführende Links
- Personenseiten
  
  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
  
  Informatik und Gestaltung
- Forschungsfeld
  
  Data Science und Künstliche Intelligenz
- Fachbereich
  
  Fachbereich 3: Information und Kommunikation
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Data-Driven Analysis of Physical and Mental Rotation Strategies

Wetzel, S., & Bertel, S. (2024). Data-Driven Analysis of Physical and Mental Rotation Strategies. CogSci 2025 conference. Rotterdam: Cognitive Science Society.

Abstract

Studying physical rotation (i.e., rotation tasks during which figures can be physically rotated, such as through gestures) can offer insights also into problem solving processes at work during mental rotation. We present a novel method for behavioral pattern analysis which we applied to data from 2,999 physical rotation tasks gathered in-class from 50 secondary school students. The method uses normalized, resampled, time-dependent data on angular offsets between figures over time and agglomerative, correlation-based clustering. Each cluster represents a distinct behavioral pattern and its respective prototype a problem solving strategy. Results indicate that multiple strategies were employed: The dominant strategy matches the classical model of mental rotation, in which angular offsets between figures are decreased over time. For the secondary strategy, angular offsets were actually increased. A subsequent analysis shows that the secondary strategy was more frequently used for symmetric figures, possibly indicating problems with correctly matching segments across figures.
- Suche auf Google Scholar
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Weiterführende Links
- Personenseiten
  
  Bertel, Sven
- Forschungsprojekt
  
  Kompetenzen für eine digitale Welt: partizipative Entwicklung von Apps zum Training räumlicher Fähigkeiten im Unterricht
- Abteilung/Einrichtung
  
  CIVU – Center for Interaction, Visualization and Usability
- Profilbereich
  
  Informatik und Gestaltung
- Forschungsfeld
  
  Human-Computer-Interaction
- Fachbereich
  
  Fachbereich 3: Information und Kommunikation
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Training for Ultrasound Imaging: An Evaluation of SonoGame

Gürgan, S., Henze, V., Gall, C., Schoenfisch, B., Brucker, S., Hahn, M., … Teistler, M. (2024). Training for Ultrasound Imaging: An Evaluation of SonoGame. ACM Symposium on Spatial User Interaction (SUI 2024). Trier, Germany: ACM, New York. http://doi.org/10.1145/3677386.3688880
- Suche auf Google Scholar
- DOI Verweis
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- Personenseiten
  
  Bertel, Sven
  
  Teistler, Michael
- Abteilung/Einrichtung
  
  CIVU – Center for Interaction, Visualization and Usability
- Profilbereich
  
  Informatik und Gestaltung
- Forschungsfeld
  
  Human-Computer-Interaction
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  Fachbereich 3: Information und Kommunikation
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Problem Solving Strategies in Physical and Mental Rotation

Wetzel, S., & Bertel, S. (2024). Problem Solving Strategies in Physical and Mental Rotation. Spatial Cognition Conference 2024. Dublin.
- Suche auf Google Scholar
Exportformate
Weiterführende Links
- Personenseiten
  
  Bertel, Sven
- Forschungsprojekt
  
  Kompetenzen für eine digitale Welt: partizipative Entwicklung von Apps zum Training räumlicher Fähigkeiten im Unterricht
- Abteilung/Einrichtung
  
  CIVU – Center for Interaction, Visualization and Usability
- Profilbereich
  
  Informatik und Gestaltung
- Forschungsfeld
  
  Human-Computer-Interaction
- Fachbereich
  
  Fachbereich 3: Information und Kommunikation
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