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2024

Why state-of-the-art analytical models for eddy current losses in PM of PMSM are insufficient for variable speed motors

Königs, M. ., & Löhlein, B. . (2024). Why state-of-the-art analytical models for eddy current losses in PM of PMSM are insufficient for variable speed motors. e+i Elektrotechnik Und Informationstechnik, 141. http://doi.org/10.1007/s00502-024-01207-y
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2024

Journal Article

Mike Königs

Bernd Löhlein
Analysis of Turbine Yaw Misalignment Estimated by LIDAR Assuming Homogeneous Flow

Zhang, Z. ., Guo, F. ., Schlipf, D. ., Schito, P. ., & Zasso, A. . (2024). Analysis of Turbine Yaw Misalignment Estimated by LIDAR Assuming Homogeneous Flow. Wind Energy Science Discussions, 2024, 1–21. http://doi.org/10.5194/wes-2023-162
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2024

Journal Article

Z. Zhang

F. Guo

D. Schlipf

P. Schito

A. Zasso
Breast cancer survival prediction using an automated mitosis detection pipeline

Stathonikos, N. ., Aubreville, M. ., de Vries, S. ., Wilm, F. ., Bertram, C. A., Veta, M. ., & van Diest, P. J. (2024). Breast cancer survival prediction using an automated mitosis detection pipeline. The Journal of Pathology: Clinical Research, 10, e70008. http://doi.org/https://doi.org/10.1002/2056-4538.70008

Abstract

Abstract Mitotic count (MC) is the most common measure to assess tumor proliferation in breast cancer patients and is highly predictive of patient outcomes. It is, however, subject to inter- and intraobserver variation and reproducibility challenges that may hamper its clinical utility. In past studies, artificial intelligence (AI)-supported MC has been shown to correlate well with traditional MC on glass slides. Considering the potential of AI to improve reproducibility of MC between pathologists, we undertook the next validation step by evaluating the prognostic value of a fully automatic method to detect and count mitoses on whole slide images using a deep learning model. The model was developed in the context of the Mitosis Domain Generalization Challenge 2021 (MIDOG21) grand challenge and was expanded by a novel automatic area selector method to find the optimal mitotic hotspot and calculate the MC per 2 mm2. We employed this method on a breast cancer cohort with long-term follow-up from the University Medical Centre Utrecht (N = 912) and compared predictive values for overall survival of AI-based MC and light-microscopic MC, previously assessed during routine diagnostics. The MIDOG21 model was prognostically comparable to the original MC from the pathology report in uni- and multivariate survival analysis. In conclusion, a fully automated MC AI algorithm was validated in a large cohort of breast cancer with regard to retained prognostic value compared with traditional light-microscopic MC.
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Aubreville, Marc

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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2024

Journal Article

Nikolas Stathonikos

Marc Aubreville

Sjoerd de Vries

Frauke Wilm

Christof Bertram

Mitko Veta

Paul van Diest

artificial intelligence

machine learning

histopathology

prognosis

mitosis

outcome

deep learning
TorqTwin—An open-source reference multibody modeling framework for wind turbine structural dynamics

Guo, F. ., Gao, Z. ., & Schlipf, D. . (2024). TorqTwin—An open-source reference multibody modeling framework for wind turbine structural dynamics. Renewable Energy, 235, 121268. http://doi.org/10.1016/j.renene.2024.121268
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Schlipf, David
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2024

Journal Article

Feng Guo

Zhen Gao

David Schlipf
Information mismatch in PHH3-assisted mitosis annotation leads to interpretation shifts in H&E slide analysis

Ganz, J. ., Marzahl, C. ., Ammeling, J. ., Rosbach, E. ., Richter, B. ., Puget, C. ., … Aubreville, M. . (2024). Information mismatch in PHH3-assisted mitosis annotation leads to interpretation shifts in H&E slide analysis. Scientific Reports, 14, 26273. http://doi.org/10.1038/s41598-024-77244-6

Abstract

Abstract The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&E)-stained slides is an important prognostic marker, as it is a measure for tumor cell proliferation. However, the identification of MFs has a known low inter-rater agreement. In a computer-aided setting, deep learning algorithms can help to mitigate this, but they require large amounts of annotated data for training and validation. Furthermore, label noise introduced during the annotation process may impede the algorithms’ performance. Unlike H&E, where identification of MFs is based mainly on morphological features, the mitosis-specific antibody phospho-histone H3 (PHH3) specifically highlights MFs. Counting MFs on slides stained against PHH3 leads to higher agreement among raters and has therefore recently been used as a ground truth for the annotation of MFs in H&E. However, as PHH3 facilitates the recognition of cells indistinguishable from H&E staining alone, the use of this ground truth could potentially introduce an interpretation shift and even label noise into the H&E-related dataset, impacting model performance. This study analyzes the impact of PHH3-assisted MF annotation on inter-rater reliability and object level agreement through an extensive multi-rater experiment. Subsequently, MF detectors, including a novel dual-stain detector, were evaluated on the resulting datasets to investigate the influence of PHH3-assisted labeling on the models’ performance. We found that the annotators’ object-level agreement significantly increased when using PHH3-assisted labeling (F1: 0.53 to 0.74). However, this enhancement in label consistency did not translate to improved performance for H&E-based detectors, neither during the training phase nor the evaluation phase. Conversely, the dual-stain detector was able to benefit from the higher consistency. This reveals an information mismatch between the H&E and PHH3-stained images as the cause of this effect, which renders PHH3-assisted annotations not well-aligned for use with H&E-based detectors. Based on our findings, we propose an improved PHH3-assisted labeling procedure.
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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

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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2024

Journal Article

Jonathan Ganz

Christian Marzahl

Jonas Ammeling

Emely Rosbach

Barbara Richter

Chloé Puget

Daniela Denk

Elena Demeter

Flaviu Tăbăran

Gabriel Wasinger

Karoline Lipnik

Marco Tecilla

Matthew Valentine

Michael Dark

Niklas Abele

Pompei Bolfa

Ramona Erber

Robert Klopfleisch

Sophie Merz

Taryn Donovan

Samir Jabari

Christof Bertram

Katharina Breininger

Marc Aubreville
Nuclear pleomorphism in canine cutaneous mast cell tumors: Comparison of reproducibility and prognostic relevance between estimates, manual morphometry, and algorithmic morphometry

Haghofer, A. ., Parlak, E. ., Bartel, A. ., Donovan, T. ., Assenmacher, C.-A. ., Bolfa, P. ., … Bertram, C. . (2024). Nuclear pleomorphism in canine cutaneous mast cell tumors: Comparison of reproducibility and prognostic relevance between estimates, manual morphometry, and algorithmic morphometry. Veterinary Pathology, 03009858241295399. http://doi.org/10.1177/03009858241295399

Abstract

Variation in nuclear size and shape is an important criterion of malignancy for many tumor types; however, categorical estimates by pathologists have poor reproducibility. Measurements of nuclear characteristics can improve reproducibility, but current manual methods are time-consuming. The aim of this study was to explore the limitations of estimates and develop alternative morphometric solutions for canine cutaneous mast cell tumors (ccMCTs). We assessed the following nuclear evaluation methods for accuracy, reproducibility, and prognostic utility: (1) anisokaryosis estimates by 11 pathologists; (2) gold standard manual morphometry of at least 100 nuclei; (3) practicable manual morphometry with stratified sampling of 12 nuclei by 9 pathologists; and (4) automated morphometry using deep learning–based segmentation. The study included 96 ccMCTs with available outcome information. Inter-rater reproducibility of anisokaryosis estimates was low (k = 0.226), whereas it was good (intraclass correlation = 0.654) for practicable morphometry of the standard deviation (SD) of nuclear size. As compared with gold standard manual morphometry (area under the ROC curve [AUC] = 0.839, 95% confidence interval [CI] = 0.701–0.977), the prognostic value (tumor-specific survival) of SDs of nuclear area for practicable manual morphometry and automated morphometry were high with an AUC of 0.868 (95% CI = 0.737–0.991) and 0.943 (95% CI = 0.889–0.996), respectively. This study supports the use of manual morphometry with stratified sampling of 12 nuclei and algorithmic morphometry to overcome the poor reproducibility of estimates. Further studies are needed to validate our findings, determine inter-algorithmic reproducibility and algorithmic robustness, and explore tumor heterogeneity of nuclear features in entire tumor sections.
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Aubreville, Marc

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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2024

Journal Article

Andreas Haghofer

Eda Parlak

Alexander Bartel

Taryn Donovan

Charles-Antoine Assenmacher

Pompei Bolfa

Michael Dark

Andrea Fuchs-Baumgartinger

Andrea Klang

Kathrin Jäger

Robert Klopfleisch

Sophie Merz

Barbara Richter

Yvonne Schulman

Hannah Janout

Jonathan Ganz

Josef Scharinger

Marc Aubreville

Stephan Winkler

Matti Kiupel

Christof Bertram
A federated learning approach to QoS forecasting in cellular vehicular communications: Approaches and empirical evidence

Baganal-Krishna, N. ., Lübben, R. ., Liotou, E. ., Katsaros, K. V., & Rizk, A. . (2024). A federated learning approach to QoS forecasting in cellular vehicular communications: Approaches and empirical evidence. Computer Networks, 242, 110239. http://doi.org/https://doi.org/10.1016/j.comnet.2024.110239

Abstract

QoS forecasting for cellular vehicular communications allows cooperative, connected and automated mobility applications to tailor their behavior to the expected communication conditions on the road. In a nutshell, vehicles may, for example, execute cooperative maneuvers if the communication quality of service is only above a certain quantitative level whereas if not they revert to the individual autonomous mode. In this paper, we propose and show empirical methods for estimating packet-based QoS metrics obtained from 5G network measurements with a direct application to vehicular applications. As many distributed vehicular applications possess strict QoS requirements, we focus here on bounding packet-based statistical QoS quantiles, specifically for latency and loss. Our approach is based on training regression neural networks in a federated learning fashion and show that it can obtain predictions on par with centralized training without the vehicles needing to transmit raw measurement data. In contrast to QoS prediction using physical layer information, we briefly discuss the embedding of such much simpler application-level service within the 5G architecture. We also validate our approach through recovering classical closed-form delay quantiles that are obtained from analytical models of simple queueing systems. We show that our approach goes beyond these simple models in that it provides quantile estimates for the complex scenario of cellular vehicle communications and under different application traffic patterns including empirical data traffic traces as well as 5G testbed measurements.
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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

Journal Article

Nehal Baganal-Krishna

Ralf Lübben

Eirini Liotou

Konstantinos Katsaros

Amr Rizk

Predictive QoS

Vehicular communications

Federated learning

5G measurements
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.
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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

Informatik und Gestaltung

Forschungsfeld

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 3: Information und Kommunikation
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2024

Journal Article

Jonas Ammeling

Marc Aubreville

Alexis Fritz

Angelika Kießig

Sebastian Krügel

Matthias Uhl
Investigation of asymmetric SVM switching patterns with minor flux loops in inverter fed induction motors

Königs, M. ., & Löhlein, B. . (2024). Investigation of asymmetric SVM switching patterns with minor flux loops in inverter fed induction motors. Research Gate, Open Access. http://doi.org/http://dx.doi.org/10.13140/RG.2.2.21923.26407
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2024

Journal Article

Mike Königs

Bernd Löhlein

2023

SonoGame

Gürgan, S. ., Bertel, S. ., Gall, C. ., Henze, V. ., Mayer, L. ., Seller, A. ., … Hahn, M. . (2023). SonoGame. Ultraschall Med, 44(S 01). http://doi.org/https://doi.org/10.1055/s-0043-1772423
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Bertel, Sven

Teistler, Michael

Forschungsprojekt

Entwicklung und praktischer Einsatz virtueller und spielbasierter Lernsysteme in der medizinischen Bildgebung

Abteilung/Einrichtung

CIVU – Center for Interaction, Visualization and Usability

Profilbereich

Informatik und Gestaltung

Forschungsfeld

Digitalisierung im Gesundheitswesen

Human-Computer-Interaction

Fachbereich

Fachbereich 3: Information und Kommunikation
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2023

Journal Article

Selin Gürgan

Sven Bertel

Christian Gall

Victoria Henze

Lukas Mayer

Anna Seller

Matthias Süncksen

Michael Teistler

Markus Hahn

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2024

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