Publikationen

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Forschungsprojekt

Institut/Einrichtung

2025

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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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
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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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

Abstract

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

2024

QUIC(K) communication for GPU Virtualization in Edge Computing

Lübben, R. ., Gaikwad, N. B., Stefanović, Čedomir ., & Kosta, S. . (2024). QUIC(K) communication for GPU Virtualization in Edge Computing. In International Conference on Wireless and Mobile Computing, Networking and Communications. Paris: IEEE. (Original work published Oktober 2024)
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Lübben, Ralf

Abteilung/Einrichtung

FLAIR - Flensburg Artificial Intelligence Research

Forschungsfeld

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 4: Wirtschaft
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2024

Conference Paper

Ralf Lübben

Nikhil Gaikwad

Čedomir Stefanović

Sokol Kosta
The Use of Machine Learning in Modeling Video Quality Assessment

Klink, J. ., Łuczyński, M. ., & Uhl, T. . (2024). The Use of Machine Learning in Modeling Video Quality Assessment. In 32nd International Conference on Software, Telecommunications and Computer Networks (S. 6). Split-Bol/Croatia: IEEE Xplore. http://doi.org/10.23919/SoftCOM62040.2024.10722023 (Original work published September 2024)
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Uhl, Tadeus

Forschungsfeld

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 3: Information und Kommunikation
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2024

Conference Paper

Janusz Klink

Michał Łuczyński

Tadeus Uhl
Automatically generated state observers – toolchain and validation with real measurements

Geisler, J. . (2024). Automatically generated state observers – toolchain and validation with real measurements. In Torque 2024. Florenz: IOP Publishing. http://doi.org/10.1088/1742-6596/2767/3/032003 (Original work published Juni 2024)

Abstract

State estimation of wind turbines is the basis for many advanced control strategies and analysis applications such as condition monitoring, fault diagnosis, and much more. It depends on the application and its goals, which states of the real turbine are relevant. Therefore, estimators with different levels of detail and sets of states suitable for the problem at hand are needed. This paper describes a toolchain that generates custom-tailored code for an extended Kalman filter. The aim is to create computationally highly efficient estimators for a wide range of applications. Automatic generation of source code allows for agile experimentation and reduces the risk of time-consuming implementation errors. The new contributions are the selection and combination of all parts of the toolchain, the extension of the integration algorithm to include the Jacobian of the transition function, and the adaptation of an efficient covariance auto-tuning algorithm. Four different models are validated in a three-stage process, including measurement data from a real turbine.
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Geisler, Jens

Profilbereich

Erneuerbare Energien und nachhaltige Entwicklung

Forschungsfeld

Wind

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 2: Energy and Life Science
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2024

Conference Paper

Jens Geisler
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.
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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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2024

Conference Paper

Henner Bendig

Data-driven Methods

Immersive Technologies

Spatial Skills

biocybernetic control loop
Impact of motions on floating wind turbine power production

Garcia-Sagrado, A. ., Schlipf, D. ., Brovia, S. P., Burstein, J. ., & Yoshinaga, T. . (2024). Impact of motions on floating wind turbine power production. In Journal of Physics: Conference Series. http://doi.org/10.1088/1742-6596/2767/6/062034
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Schlipf, David
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2024

Conference Paper

Ana Garcia-Sagrado

David Schlipf

Santiago Brovia

Justin Burstein

Tsuyoshi Yoshinaga

2023

Improving the Quality of Wind Field Reconstruction Techniques for Lidar-assisted Control of Wind Turbines

Afanasieva, N. ., & Schlipf, D. . (2023). Improving the Quality of Wind Field Reconstruction Techniques for Lidar-assisted Control of Wind Turbines. In 19thEAWE PhD Seminar on Wind Energy. http://doi.org/10.5281/zenodo.8335186 (Original work published 2025)
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Schlipf, David
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2023

Conference Paper

Nadiia Afanasieva

David Schlipf
The performance of two control strategies for floating wind turbines: lidar-assisted feedforward and multi-variable feedback

Guo, F. ., Schlipf, D. ., Lemmer, F. ., Raach, S. ., Özinan, U. ., Adam, R. ., & Choisnet, T. . (2023). The performance of two control strategies for floating wind turbines: lidar-assisted feedforward and multi-variable feedback. In Journal of Physics: Conference Series (Bd. 2626, S. 012005). http://doi.org/10.1088/1742-6596/2626/1/012005 (Original work published 2025)
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Schlipf, David
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2023

Conference Paper

Feng Guo

David Schlipf

Frank Lemmer

Steffen Raach

Umut Özinan

Raphaël Adam

Thomas Choisnet

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2025

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