Publikationen

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Keyword

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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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Banerjee, Sweta

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

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
Chromosome Mask-Conditioned Generative Inpainting for Atypical Mitosis Classification

Banerjee, S., Weiss, V., Conrad, T., Donovan, T. A., Ammeling, J., Fick, R., … Aubreville, M. (2025). Chromosome Mask-Conditioned Generative Inpainting for Atypical Mitosis Classification. In Proceedings of the MICCAI Workshop on Computational Pathology (Bd. 316, S. 266–277). PMLR.
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Banerjee, Sweta

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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2025

Conference Paper

Sweta Banerjee

Viktoria Weiss

Thomas Conrad

Taryn Donovan

Jonas Ammeling

Rutger Fick

Jonas Utz

Robert Klopfleisch

Christopher Kaltenecker

Christof Bertram

Katharina Breininger

Marc Aubreville
MeasureTip: Towards a Finger-Worn Measurement Device

Olberding, S., & Wallbaum, T. (2025). MeasureTip: Towards a Finger-Worn Measurement Device. In Proceedings of the 2025 Mensch und Computer 2025 (S. 673–677).
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Wallbaum, Torben
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2025

Conference Paper

Simon Olberding

Torben Wallbaum
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

(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

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
Effortless Vision-Language Model Specialization in Histopathology without Annotation

Qiu, J., Jain, N., Ammeling, J., Aubreville, M., & Breininger, K. (2025). Effortless Vision-Language Model Specialization in Histopathology without Annotation. In MICCAI Workshop on Computational Pathology with Multimodal Data (COMPAYL). Abgerufen von https://openreview.net/forum?id=pQDzXVeBja
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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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2025

Conference Paper

Jingna Qiu

Nishanth Jain

Jonas Ammeling

Marc Aubreville

Katharina Breininger
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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Banerjee, Sweta

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

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
Open-Source Web Dashboard zur Simulation, Analyse und Bewertung von Wärmepumpen

Fritz, M., Freißmann, J., & Tuschy, I. (2025). Open-Source Web Dashboard zur Simulation, Analyse und Bewertung von Wärmepumpen. In I. Weidlich, P. Vuthi, & C. Lange (Hrsg.), Tagungsband (2. Version) — 2. Konferenz der Norddeutschen Wärmeforschung: Gemeinsam die Wärmewende voranbringen. (S. 24–33). HafenCity Universität. http://doi.org/10.34712/142.68
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Fritz, Malte

Freißmann, Jonas

Tuschy, Ilja
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2025

Conference Paper

Malte Fritz

Jonas Freißmann

Ilja Tuschy

Ingo Weidlich

Petrit Vuthi

Constantin Lange

Wärmewende

Wärmeplanung

Fernwärmeversorgung

Energieplanung

Wärmeforschung

Wohngebäude
Relatedness Technologies: An Online Compendium and Systematic Review

Wenhart, C., Ringfort-Felner, R., Wallbaum, T., Amidi, M., Albers, R., & Hassenzahl, M. (2025). Relatedness Technologies: An Online Compendium and Systematic Review. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. New York, NY, USA: Association for Computing Machinery. http://doi.org/10.1145/3706598.3714260

Abstract

Over the past decades, numerous concepts and prototypes for fostering emotional connections across distance (relatedness technologies) have been proposed. This has made it challenging for researchers and designers in Human-Computer Interaction (HCI) to maintain a comprehensive overview and effectively build on previous work. To address this, we conducted a systematic literature search (PRISMA) and collected 241 concepts and prototypes (2010-2024). We organized this corpus according to key aspects: (1) target population, (2) theoretical grounding, (3) design, (4) evaluation, and (5) ethics. Based on this, we developed the “COmpendium of RElatedness Technologies” (CORE), an open-access, searchable online database that provides researchers and practitioners with a reliable repository to inform future work. In addition, we present a systematic review of the corpus, revealing that despite its long tradition work on relatedness technologies remains characterized by limited theoretical grounding, lack of robust empirical evidence of effects, and insufficient attention to ethical considerations.
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Wallbaum, Torben

Amidi, Maryam
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2025

Conference Paper

Christiane Wenhart

Ronda Ringfort-Felner

Torben Wallbaum

Maryam Amidi

Ruben Albers

Marc Hassenzahl

Connectedness

Database

Experience Design

Relatedness

Relatedness Technology

Systematic Review

2024

QUIC(K) communication for GPU Virtualization in Edge Computing

Lübben, R., Gaikwad, N. B., Stefanović, Č., & 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

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2025

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