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2026

Benchmarking Foundation Models for Mitotic Figure Classification

Ammeling, J., Ganz, J., Rosbach, E., Lausser, L., Bertram, C. A., Breininger, K., & Aubreville, M. (2026). Benchmarking Foundation Models for Mitotic Figure Classification. Machine Learning for Biomedical Imaging, 3, 38–58. http://doi.org/"https://doi.org/10.59275/j.melba.2026-a3eb"
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Aubreville, Marc

Forschungsprojekt

(mu)ROMI: 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

Gesundheit und Life Sciences

Forschungsfeld

Data Science und Künstliche Intelligenz

Fachbereich

Fachbereich 3: Information und Kommunikation
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2026

Journal Article

Jonas Ammeling

Jonathan Ganz

Emely Rosbach

Ludwig Lausser

Christof Bertram

Katharina Breininger

Marc Aubreville
Evaluation of ChatGPT Study Mode: Results from an Expert Rating Regarding Self-Regulated Learning

Steinberg, E., Perels, F., Aubreville, M., & Dörrenbächer-Ulrich, L. (2026). Evaluation of ChatGPT Study Mode: Results from an Expert Rating Regarding Self-Regulated Learning. Open Education Studies, 8, 20250169. http://doi.org/10.1515/edu-2025-0169
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Aubreville, Marc

Abteilung/Einrichtung

CIVU – Center for Interaction, Visualization and Usability

FLAIR - Flensburg Artificial Intelligence Research

Fachbereich

Fachbereich 3: Information und Kommunikation
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2026

Journal Article

Evelyn Steinberg

Franziska Perels

Marc Aubreville

Laura Dörrenbächer-Ulrich
Großwärmepumpen für den kurz- bis mittelfristigen Einsatz in multivalenten Wärmeversorgungssystemen

Freißmann, J., Fritz, M., & Tuschy, I. (2026). Großwärmepumpen für den kurz- bis mittelfristigen Einsatz in multivalenten Wärmeversorgungssystemen. In J. Knies, T. Schwarz, & Y. E. Gerling (Hrsg.), Tagungsband zur 3. Konferenz der Norddeutschen Wärmeforschung (S. 54–59). Hochschule Bremen. http://doi.org/10.26092/elib/5016
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Freißmann, Jonas

Fritz, Malte

Tuschy, Ilja
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2026

Conference Paper

Jonas Freißmann

Malte Fritz

Ilja Tuschy

Jürgen Knies

Timmy Schwarz

Yannik Gerling

Wärmewende

Wärmeforschung

Energiewende

Fernwärme

Nahwärme

Transformation

Wärmeversorgung

Energieversorgung

Wärmepumpe

Wohngebäude

Wärmeplanung

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Wasserstoff

Energiespeicher
Data set creation for supervised deep learning–based analysis of microscopic images: Review of important considerations and recommendations

Bertram, C. A., Weiss, V., Ammeling, J., Schabel, M., Donovan, T. A., Wilm, F., … Aubreville, M. (2026). Data set creation for supervised deep learning–based analysis of microscopic images: Review of important considerations and recommendations. Veterinary Pathology, 03009858261457959. http://doi.org/10.1177/03009858261457959

Abstract

Supervised deep learning (DL) receives great interest for automated analysis of microscopic images with an increasing body of literature supporting its potential. The development and testing of those DL models rely heavily on the availability of high-quality, large-scale data sets. However, creating such data sets is a complex and resource-intensive process, often hindered by challenges such as time constraints, domain variability, and risks of bias in image collection and label creation. This review provides a comprehensive guide to the critical steps in data set creation, including (1) image acquisition, (2) selection of annotation software, and (3) annotation creation. For image acquisition, besides ensuring a sufficiently large number, it is important to address sources of image variability (domain shifts), such as those related to slide preparation and digitization, that could lead to algorithmic errors if not adequately represented in the training data. For annotations, key quality criteria are the 3 “C”s: correctness, consistency, and completeness. For mitigation of annotation bias of a single annotator, this review explores advanced annotation methods (eg, computer-assisted annotations). To support data set creators, a standard operating procedure is provided as supplemental material, summarizing all important considerations for data set creation. Furthermore, this article underscores the importance of open data sets in driving innovation and enhancing reproducibility of DL research. By addressing the challenges and offering practical recommendations, this review aims to advance the creation and availability of high-quality, large-scale data sets, ultimately contributing to the development of generalizable and robust DL models for pathology applications.
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Aubreville, Marc

Abteilung/Einrichtung

FLAIR - Flensburg Artificial Intelligence Research

Profilbereich

Informatik und Gestaltung

Fachbereich

Fachbereich 3: Information und Kommunikation
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2026

Journal Article

Christof Bertram

Viktoria Weiss

Jonas Ammeling

Maria Schabel

Taryn Donovan

Frauke Wilm

Christian Marzahl

Katharina Breininger

Marc Aubreville
Optimisation of the Hydraulic Variable Inertia Flywheel for Specific Energy

Rettig, A., Reese, L., & Jauch, C. (2026). Optimisation of the Hydraulic Variable Inertia Flywheel for Specific Energy. In 19. Symposium Energieinnovation 2026. Östereich, Graz. http://doi.org/https://doi.org/10.13140/RG.2.2.10150.59203

Abstract

The ongoing transition towards inverter-based generation is significantly reducing conventional rotational inertia in power systems, leading to increased rates of change of frequency. To address this challenge, the Hydraulic Variable Inertia Flywheel (HVI-FW) represents a novel alternative for distributed and inherent inertia provision. This paper presents a design tool developed to optimize the geometric parameters of the novel flywheel, with respect to specific energy. A parameter study is conducted by varying key geometric dimensions, such as radius and height. The results demonstrate that the HVI-FW can achieve significantly higher specific energy than conventional flywheels, particularly for small radii, making it well suited for coupling with small, synchronously rotating electrical machines. The findings highlight the potential of the HVI-FW as an effective solution for providing distributed inertia in future low-inertia power systems.
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Jauch, Clemens

Forschungsprojekt

Inno!Nord-HYDRAD - Hydraulischer Schwungradspeicher

Abteilung/Einrichtung

Wind Energy Technology Institute

Profilbereich

Erneuerbare Energien und nachhaltige Entwicklung

Forschungsfeld

Netzintegration

Fachbereich

Fachbereich 2: Energy and Life Science
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2026

Conference Paper

Arne Rettig

Lisanne Reese

Prof. Dr. Clemens Jauch

Flywheel, Inertia, Power System Inertia, Energy Storage, Electrical Machines
Open Source Web Dashboard zur Unterstützung der Konzeption von Wärmeversorgungssystemen

Fritz, M., Freißmann, J., & Tuschy, I. (2026). Open Source Web Dashboard zur Unterstützung der Konzeption von Wärmeversorgungssystemen. In J. Knies, T. Schwarz, & Y. E. Gerling (Hrsg.), Tagungsband zur 3. Konferenz der Norddeutschen Wärmeforschung (S. 33–38). Hochschule Bremen. http://doi.org/10.26092/elib/5016
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Fritz, Malte

Freißmann, Jonas

Tuschy, Ilja
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2026

Conference Paper

Malte Fritz

Jonas Freißmann

Ilja Tuschy

Jürgen Knies

Timmy Schwarz

Yannik Gerling

Wärmewende

Wärmeforschung

Energiewende

Fernwärme

Nahwärme

Transformation

Wärmeversorgung

Energieversorgung

Wärmepumpe

Wohngebäude

Wärmeplanung

Wärmespeicher

Wasserstoff

Energiespeicher
Reporting Transparency in Veterinary Pathology Deep Learning: A Systematic Review of Reproducibility-Critical Details

Banerjee, S., Bertram, C. A., Weiss, V., Ammeling, J., Conrad, T., Porsche, N., … Aubreville, M. (2026). Reporting Transparency in Veterinary Pathology Deep Learning: A Systematic Review of Reproducibility-Critical Details. Veterinary Pathology. http://doi.org/10.1177/03009858261459452
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Banerjee, Sweta

Porsche, Nils

Aubreville, Marc

Forschungsprojekt

(mu)ROMI: 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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2026

Journal Article

Sweta Banerjee

Christof Bertram

Viktoria Weiss

Jonas Ammeling

Thomas Conrad

Nils Porsche

Robert Klopfleisch

Christoph Stoblberger

Christopher Kaltenecker

Katharina Breininger

Marc Aubreville

2025

Artificial intelligence predicts c-KIT exon 11 genotype by phenotype in canine cutaneous mast cell tumors: Can human observers learn it?

Puget, C., Ganz, J., Bertram, C. A., Conrad, T., Baeblich, M., Voss, A., … Klopfleisch, R. (2025). Artificial intelligence predicts c-KIT exon 11 genotype by phenotype in canine cutaneous mast cell tumors: Can human observers learn it? Veterinary Pathology, 03009858251380284. http://doi.org/10.1177/03009858251380284 (Original work published 2026)
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Aubreville, Marc

Abteilung/Einrichtung

FLAIR - Flensburg Artificial Intelligence Research

CIVU – Center for Interaction, Visualization and Usability

Profilbereich

Gesundheit und Life Sciences

Fachbereich

Fachbereich 3: Information und Kommunikation
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2025

Journal Article

Chloé Puget

Jonathan Ganz

Christof Bertram

Thomas Conrad

Malte Baeblich

Anne Voss

Katharina Landmann

Alexander Haake

Andreas Spree

Svenja Hartung

Leonore Aeschlimann

Sara Soto

Simone De Brot

Martina Dettwiler

Heike Aupperle-Lellbach

Pompei Bolfa

Alexander Bartel

Matti Kiupel

Katharina Breininger

Marc Aubreville

Robert Klopfleisch
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 2026)
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Schlipf, David

Fachbereich

Fachbereich 2: Energy and Life Science
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2025

Miscellaneous

David Schlipf
Challenges of top–down flexibility deployment for grid expansion across all voltage levels

Büttner, C., Esterl, K., Schachler, B., & Cußmann, I. (2025). Challenges of top–down flexibility deployment for grid expansion across all voltage levels. Environmental Research: Energy, 2, 045017. http://doi.org/10.1088/2753-3751/ae2686 (Original work published 2026)

Abstract

The energy transition in Germany demands a comprehensive transformation of electricity grids at all levels, making them central to the achievement of climate goals and the integration of renewable energy in all energy sectors. However, grid infrastructure currently represents a critical bottleneck. In particular, the role of medium and low voltage grids is changing. Historically designed to distribute electricity from the transmission system to demands, medium and low voltage grids must now adapt to accommodate distributed generation, increased flexibility, and new demands driven by sector coupling. Flexibility options, such as demand response and energy storage, offer potential solutions, but their efficient deployment requires optimized coordination. In order to contribute to the identification of an effective coordination strategy, the open-source tool eGo (electricity grid optimization) has been developed. The objective is to quantify the expansion needs of the grid at all levels in Germany, considering flexibility options from the sectors electricity, heat, gas, and mobility using a top–down approach. The method is applied to a mid-term scenario depicting the year 2035. The results show that while top–down dispatch optimization is effective for extra-high and high voltage grids, it can exacerbate expansion requirements in medium and low voltage grids. As a result, flexibility options can reduce system costs by only 1\%, taking into account the need for expansion at all grid levels. The curtailment of intermittent renewables can be significantly reduced by 71\%. Coordinated dispatch across grid operators and levels is therefore essential to fully leverage flexibility options, ensuring that the energy transition proceeds efficiently across the entire electricity grid.
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eGon
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2025

Journal Article

Clara Büttner

Katharina Esterl

Birgit Schachler

Ilka Cußmann

Publikationen

2026

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