Marc Aubreville

EDBT 2026 Demo / reviewers in the wild / expert

dblp:166/6549 · DBLP profile ↗

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13ranked-venue papers

3as first author

10since 2021 · last 2026

0000-0002-5294-5247ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 7 · 1 first-author · 4 since 2021Applied, interdisciplinary, general and emerging computing · 7 · 2 first-author · 6 since 2021Artificial intelligence and machine learning · 2 · 2 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Year	Publication	Venue	Position
2026	Decomposition Sampling for Efficient Region Annotations in Active Learning abstract Active learning improves annotation efficiency by selecting the most informative samples for annotation and model training. While most prior work has focused on selecting informative images for classification tasks, we investigate the more challenging setting of dense prediction, where annotations are more costly and time-intensive, especially in medical imaging. Region-level annotation has been shown to be more efficient than image-level annotation for these tasks. However, existing methods for representative annotation region selection suffer from high computational and memory costs, irrelevant region choices, and heavy reliance on uncertainty sampling. We propose decomposition sampling (DECOMP), a new active learning sampling strategy that addresses these limitations. It enhances annotation diversity by decomposing images into class-specific components using pseudo-labels and sampling regions from each class. Class-wise predictive confidence further guides the sampling process, ensuring that difficult classes receive additional annotations. Across ROI classification, 2-D segmentation, and 3-D segmentation, DECOMP consistently surpasses baseline methods by better sampling minority-class regions and boosting performance on these challenging classes. Code is in https://github.com/JingnaQiu/DECOMP.git. Jingna Qiu, Frauke Wilm, Mathias Öttl, Jonas Utz, Maja Schlereth, Moritz Schillinger, Marc Aubreville, Katharina Breininger	WACV	7
2025	"When Two Wrongs Don't Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology Emely Rosbach, Jonas Ammeling, Sebastian Krügel, Angelika Kießig, Alexis Fritz, Jonathan Ganz, Chloé Puget, Taryn A. Donovan, Andrea Klang, Maximilian C. Köller, Pompei Bolfa, Marco Tecilla, Daniela Denk, Matti Kiupel, Georgios Paraschou, Mun Keong Kok, Alexander F. H. Haake, Ronald R. de Krijger, Andreas F.-P. Sonnen, Tanit Kasantikul, Gerry M. Dorrestein, Rebecca C. Smedley, Nikolas Stathonikos, Matthias Uhl, Christof Bertram, Andreas Riener, Marc Aubreville	CHI	27
2025	Re-identification from histopathology images abstract scores of up to 80.1% and 77.19% on the LSCC and LUAD datasets, respectively, and with 77.09% on our meningioma dataset. Based on our findings, we formulated a risk assessment scheme to estimate the risk to the patient's privacy prior to publication. Jonathan Ganz, Jonas Ammeling, Samir Jabari, Katharina Breininger, Marc Aubreville	Medical Image Anal.	5
2025	Investigation of Class Separability Within Object Detection Models in Histopathology abstract Object detection is one of the most common tasks in histopathological image analysis and generalization is a key requirement for the clinical applicability of deep object detection models. However, traditional evaluation metrics often fail to provide insights into why models fail on certain test cases, especially in the presence of domain shifts. In this work, we propose a novel quantitative method for assessing the discriminative power of a model's latent space. Our approach, applicable to all object detection models with known local correspondences such as the popular RetinaNet, FCOS, or YOLO approaches, allows tracing discrimination across layers and coordinates. We motivate, adapt, and evaluate two suitable metrics, the generalized discrimination value and the Hellinger distance, and incorporate them into our approach. Through empirical validation on real-world histopathology datasets, we demonstrate the effectiveness of our method in capturing model discrimination properties and providing insights for architectural optimization. This work contributes to bridging the gap between model performance evaluation and understanding the underlying mechanisms influencing model behavior. Jonas Ammeling, Jonathan Ganz, Frauke Wilm, Katharina Breininger, Marc Aubreville	IEEE Trans. Medical Imaging	5
2024	Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models abstract We introduce Würstchen, a novel architecture for text-to-image synthesis that combines competitive performance with unprecedented cost-effectiveness for large-scale text-to-image diffusion models. A key contribution of our work is to develop a latent diffusion technique in which we learn a detailed but extremely compact semantic image representation used to guide the diffusion process. This highly compressed representation of an image provides much more detailed guidance compared to latent representations of language and this significantly reduces the computational requirements to achieve state-of-the-art results. Our approach also improves the quality of text-conditioned image generation based on our user preference study. The training requirements of our approach consists of 24,602 A100-GPU hours - compared to Stable Diffusion 2.1's 200,000 GPU hours. Our approach also requires less training data to achieve these results. Furthermore, our compact latent representations allows us to perform inference over twice as fast, slashing the usual costs and carbon footprint of a state-of-the-art (SOTA) diffusion model significantly, without compromising the end performance. In a broader comparison against SOTA models our approach is substantially more efficient and compares favourably in terms of image quality. We believe that this work motivates more emphasis on the prioritization of both performance and computational accessibility. Pablo Pernias, Dominic Rampas, Mats L. Richter, Christopher Joseph Pal, Marc Aubreville	ICLR	5
2024	Leveraging Image Captions for Selective Whole Slide Image Annotation Jingna Qiu, Marc Aubreville, Frauke Wilm, Mathias Öttl, Jonas Utz, Maja Schlereth, Katharina Breininger	MICCAI (12)	2
2024	Domain generalization across tumor types, laboratories, and species - Insights from the 2022 edition of the Mitosis Domain Generalization Challenge Marc Aubreville, Nikolas Stathonikos, Taryn A. Donovan, Robert Klopfleisch, Jonas Ammeling, Jonathan Ganz, Frauke Wilm, Mitko Veta, Samir Jabari, Markus Eckstein, Jonas Annuscheit, Christian Krumnow, Engin Bozaba, Sercan Cayir, Hongyan Gu, Xiang 'Anthony' Chen, Mostafa Jahanifar, Adam J. Shephard, Satoshi Kondo, Satoshi Kasai, Sujatha Kotte, Vangala Saipradeep, Maxime W. Lafarge, Viktor H. Koelzer, Ziyue Wang 0005, Yongbing Zhang 0002, Sen Yang 0006, Katharina Breininger, Christof Bertram	Medical Image Anal.	1
2023	Why is the Winner the Best? abstract International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Tizabi, Fabian Isensee, Tim Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David Gage Ellis, Sandy Engelhardt, Melanie Ganz-Benjaminsen, Noha M. Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias P. Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, A. Emre Kavur, Oldrich Kodym, Michal Kozubek 0001, Jianning Li 0002, Hongwei Li 0004, Jun Ma 0016, Carlos Martín-Isla, Bjoern Menze, J. Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, Jonathan Rafael-Patino, Vivek Singh Bawa, Stefanie Speidel, Carole H. Sudre, Kimberlin M. H. van Wijnen, Martin Wagner 0001, D. Wei, Amine Yamlahi, Moi Hoon Yap, C. Yuan, Maximilian Zenk, A. Zia, David Zimmerer, Dogu Baran Aydogan, Binod Bhattarai, Louise Bloch, Raphael Brüngel, J. Cho, C. Choi, Qi Dou 0001, Ivan Ezhov, Christoph M. Friedrich, C. Fuller, Rebati Raman Gaire, Adrian Galdran, Álvaro García-Faura, Maria Grammatikopoulou, S. Hong, Mostafa Jahanifar, I. Jang, Abdolrahim Kadkhodamohammadi, I. Kang, Florian Kofler, S. Kondo, Hugo J. Kuijf, M. Luu, Tomaz Martincic, Pedro Morais, Mohamed A. Naser, Bruno Oliveira 0002, David Owen 0001, S. Pang, Szymon Plotka, Élodie Puybareau, Nasir M. Rajpoot, K. Ryu, Numan Saeed, Adam J. Shephard, Dejan Stepec, Ronast Subedi, Guillaume Tochon, Helena R. Torres, Hélène Urien, João L. Vilaça, Kareem A. Wahid, Benedikt Wiestler, Marek Wodzinski, F. Xia, J. Xie, Z. Xiong, Sen Yang 0006, Klaus H. Maier-Hein, Paul F. Jaeger, Annette Kopp-Schneider, Lena Maier-Hein	CVPR	9
2023	Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation Jingna Qiu, Frauke Wilm, Mathias Öttl, Maja Schlereth, Tobias Heimann, Marc Aubreville, Katharina Breininger	MICCAI (2)	7
2023	Mitosis domain generalization in histopathology images - The MIDOG challenge Marc Aubreville, Nikolas Stathonikos, Christof Bertram, Robert Klopfleisch, Natalie D. ter Hoeve, Francesco Ciompi, Frauke Wilm, Christian Marzahl, Taryn A. Donovan, Andreas K. Maier, Jack Breen, Nishant Ravikumar, Youjin Chung, Jinah Park, Ramin Nateghi, Fattaneh Pourakpour, Rutger H. J. Fick, Saima Ben Hadj, Mostafa Jahanifar, Adam J. Shephard, Jakob Dexl, Thomas Wittenberg, Satoshi Kondo, Maxime W. Lafarge, Viktor H. Koelzer, Jingtang Liang, Yubo Wang 0001, Jingxin Liu 0005, Salar Razavi, April Khademi, Sen Yang 0006, Ramona Erber, Andrea Klang, Karoline Lipnik, Pompei Bolfa, Michael J. Dark, Gabriel Wasinger, Mitko Veta, Katharina Breininger	Medical Image Anal.	1
2020	CLCNET: Deep Learning-Based Noise Reduction for Hearing aids using Complex Linear Coding abstract Noise reduction is an important part of modern hearing aids and is included in most commercially available devices. Deep learning-based state-of-the-art algorithms, however, either do not consider real-time and frequency resolution constrains or result in poor quality under very noisy conditions.To improve monaural speech enhancement in noisy environments, we propose CLCNet, a framework based on complex valued linear coding. First, we define complex linear coding (CLC) motivated by linear predictive coding (LPC) that is applied in the complex frequency domain. Second, we propose a framework that incorporates complex spectrogram input and coefficient output. Third, we define a parametric normalization for complex valued spectrograms that complies with low-latency and on-line processing.Our CLCNet was evaluated on a mixture of the EUROM database and a real-world noise dataset recorded with hearing aids and compared to traditional real-valued Wiener-Filter gains. Hendrik Schröter, Tobias Rosenkranz, Alberto N. Escalante, Marc Aubreville, Andreas K. Maier	ICASSP	4
2020	Are Fast Labeling Methods Reliable? A Case Study of Computer-Aided Expert Annotations on Microscopy Slides Christian Marzahl, Christof Bertram, Marc Aubreville, Anne Petrick, Kristina Weiler, Agnes C. Gläsel, Marco Fragoso, Sophie Merz, Florian Bartenschlager, Judith Hoppe, Alina Langenhagen, Anne-Katherine Jasensky, Jörn Voigt, Robert Klopfleisch, Andreas K. Maier	MICCAI (1)	3
2015	Directionality assessment of adaptive binaural beamforming with noise suppression in hearing aids abstract In this work we present a new method for assessment of directionality in modern hearing aids, which has the benefit of simulating both a target source and an interfering source at the same time using speech signals. We show the benefits and the limitations of the method and present measurements for different microphone modes of different manufacturers. Furthermore, a sequential index based on these measurements is introduced. Marc Aubreville, Stefan Petrausch	ICASSP	1