TY - JOUR A1 - Müller, André A1 - Lausser, Ludwig A1 - Wilhelm, Adalbert A1 - Ropinski, Timo A1 - Platzer, Matthias A1 - Neumann, Heiko A1 - Kestler, Hans A. T1 - A perceptually optimised bivariate visualisation scheme for high-dimensional fold-change data JF - Advances in Data Analysis and Classification N2 - Visualising data as diagrams using visual attributes such as colour, shape, size, and orientation is challenging. In particular, large data sets demand graphical display as an essential step in the analysis. In order to achieve comprehension often different attributes need to be displayed simultaneously. In this work a comprehensible bivariate, perceptually optimised visualisation scheme for high-dimensional data is proposed and evaluated. It can be used to show fold changes together with confidence values within a single diagram. The visualisation scheme consists of two parts: a uniform, symmetric, two-sided colour scale and a patch grid representation. Evaluation of uniformity and symmetry of the two-sided colour scale was performed in comparison to a standard RGB scale by twenty-five observers. Furthermore, the readability of the generated map was validated and compared to a bivariate heat map scheme. UR - https://doi.org/10.1007/s11634-020-00416-5 KW - colour scales KW - bivariate KW - visualisation Y1 - 2020 UR - https://doi.org/10.1007/s11634-020-00416-5 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27235 SN - 1862-5355 SN - 1862-5347 VL - 15 IS - 2 SP - 463 EP - 480 PB - Springer CY - Berlin ER - TY - JOUR A1 - Buchholz, Malte A1 - Lausser, Ludwig A1 - Schenk, Miriam A1 - Earl, Julie A1 - Lawlor, Rita T. A1 - Scarpa, Aldo A1 - Sanjuanbenito, Alfonso A1 - Carrato, Alfredo A1 - Malats, Nuria A1 - Tjaden, Christine A1 - Giese, Nathalia A. A1 - Büchler, Markus A1 - Hackert, Thilo A1 - Kestler, Hans A. A1 - Gress, Thomas M. T1 - Combined analysis of a serum mRNA/miRNA marker signature and CA 19‐9 for timely and accurate diagnosis of recurrence after resection of pancreatic ductal adenocarcinoma: A prospective multicenter cohort study JF - United European Gastroenterology Journal N2 - Background and Aims Timely and accurate detection of tumor recurrence in pancreatic ductal adenocarcinoma (PDAC) patients is an urgent and unmet medical need. This study aimed to develop a noninvasive molecular diagnostic procedure for the detection of recurrence after PDAC resection based on quantification of circulating mRNA and miRNA biomarkers in serum samples. Methods In a multicentric study, serum samples from a total of 146 patients were prospectively collected after resection. Samples were classified into a “No Evidence of Disease” and a “Recurrence” group based on clinical follow‐up data. A multianalyte biomarker panel was composed of mRNAs and miRNA markers and simultaneously analyzed in serum samples using custom microfluidic qPCR arrays (TaqMan array cards). A diagnostic algorithm was developed combining a 7‐gene marker signature with CA19‐9 data. Results The best‐performing marker combination achieved 90% diagnostic accuracy in predicting the presence of tumor recurrence (98% sensitivity; 84% specificity), clearly outperforming the singular CA 19‐9 analysis. Moreover, time series data obtained by analyzing successively collected samples from 5 patients during extended follow‐up suggested that molecular diagnosis has the potential to detect recurrence earlier than routine clinical procedures. Conclusions TaqMan array card measurements were found to be biologically valid and technically reproducible. The BioPac multianalyte marker panel is capable of sensitive and accurate detection of recurrence in patients resected for PDAC using a simple blood test. This could allow a closer follow‐up using shorter time intervals than currently used for imaging, thus potentially prompting an earlier work‐up with additional modalities to allow for earlier therapeutic intervention. This study provides a promising approach for improved postoperative monitoring of resected PDAC patients, which is an urgent and unmet clinical need. UR - https://doi.org/10.1002/ueg2.12676 Y1 - 2024 UR - https://doi.org/10.1002/ueg2.12676 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-53036 SN - 2050-6414 VL - 13 IS - 3 SP - 353 EP - 363 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Lausser, Ludwig A1 - Kubis, Lea A1 - Rottbauer, Wolfgang A1 - Frank, Derk A1 - Just, Steffen A1 - Kestler, Hans A. T1 - Semantic Multi-Classifier Systems Identify Predictive Processes in Heart Failure Models across Species JF - Biomolecules N2 - Genetic model organisms have the potential of removing blind spots from the underlying gene regulatory networks of human diseases. Allowing analyses under experimental conditions they complement the insights gained from observational data. An inevitable requirement for a successful trans-species transfer is an abstract but precise high-level characterization of experimental findings. In this work, we provide a large-scale analysis of seven weak contractility/heart failure genotypes of the model organism zebrafish which all share a weak contractility phenotype. In supervised classification experiments, we screen for discriminative patterns that distinguish between observable phenotypes (homozygous mutant individuals) as well as wild-type (homozygous wild-types) and carriers (heterozygous individuals). As the method of choice we use semantic multi-classifier systems, a knowledge-based approach which constructs hypotheses from a predefined vocabulary of high-level terms (e.g., Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways or Gene Ontology (GO) terms). Evaluating these models leads to a compact description of the underlying processes and guides the screening for new molecular markers of heart failure. Furthermore, we were able to independently corroborate the identified processes in Wistar rats. UR - https://doi.org/10.3390/biom8040158 KW - heart failure phenotypes KW - zebrafish KW - Wistar rat KW - semantic multi-classifier systems Y1 - 2018 UR - https://doi.org/10.3390/biom8040158 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27303 SN - 2218-273X N1 - This article belongs to the Special Issue "Biomolecules for Translational Approaches in Cardiology" VL - 8 (2018) IS - 4 PB - MDPI CY - Basel ER - TY - JOUR A1 - Kustermann, Monika A1 - Manta, Linda A1 - Paone, Christoph A1 - Kustermann, Jochen A1 - Lausser, Ludwig A1 - Wiesner, Cora A1 - Eichinger, Ludwig A1 - Clemen, Christoph S. A1 - Schröder, Rolf A1 - Kestler, Hans A. A1 - Sandri, Marco A1 - Rottbauer, Wolfgang A1 - Just, Steffen T1 - Loss of the novel Vcp (valosin containing protein) interactor Washc4 interferes with autophagy-mediated proteostasis in striated muscle and leads to myopathy in vivo JF - Autophagy N2 - VCP/p97 (valosin containing protein) is a key regulator of cellular proteostasis. It orchestrates protein turnover and quality control in vivo, processes fundamental for proper cell function. In humans, mutations in VCP lead to severe myo- and neuro-degenerative disorders such as inclusion body myopathy with Paget disease of the bone and frontotemporal dementia (IBMPFD), amyotrophic lateral sclerosis (ALS) or and hereditary spastic paraplegia (HSP). We analyzed here the in vivo role of Vcp and its novel interactor Washc4/Swip (WASH complex subunit 4) in the vertebrate model zebrafish (Danio rerio). We found that targeted inactivation of either Vcp or Washc4, led to progressive impairment of cardiac and skeletal muscle function, structure and cytoarchitecture without interfering with the differentiation of both organ systems. Notably, loss of Vcp resulted in compromised protein degradation via the proteasome and the macroautophagy/autophagy machinery, whereas Washc4 deficiency did not affect the function of the ubiquitin-proteasome system (UPS) but caused ER stress and interfered with autophagy function in vivo. In summary, our findings provide novel insights into the in vivo functions of Vcp and its novel interactor Washc4 and their particular and distinct roles during proteostasis in striated muscle cells. UR - https://doi.org/10.1080/15548627.2018.1491491 KW - Proteostasis KW - striated muscle KW - Vcp KW - Washc4 KW - zebrafish Y1 - 2018 UR - https://doi.org/10.1080/15548627.2018.1491491 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27311 SN - 1554-8627 VL - 14 (2018) IS - 11 SP - 1911 EP - 1927 PB - Taylor & Francis CY - London ER - TY - JOUR A1 - Hühne, Rolf A1 - Kessler, Viktor A1 - Fürstberger, Axel A1 - Werle, Silke D. A1 - Platzer, Matthias A1 - Sühnel, Jürgen A1 - Lausser, Ludwig A1 - Kestler, Hans A. T1 - 3D Network exploration and visualisation for lifespan data JF - BMC Bioinformatics N2 - Background The Ageing Factor Database AgeFactDB contains a large number of lifespan observations for ageing-related factors like genes, chemical compounds, and other factors such as dietary restriction in different organisms. These data provide quantitative information on the effect of ageing factors from genetic interventions or manipulations of lifespan. Analysis strategies beyond common static database queries are highly desirable for the inspection of complex relationships between AgeFactDB data sets. 3D visualisation can be extremely valuable for advanced data exploration. Results Different types of networks and visualisation strategies are proposed, ranging from basic networks of individual ageing factors for a single species to complex multi-species networks. The augmentation of lifespan observation networks by annotation nodes, like gene ontology terms, is shown to facilitate and speed up data analysis. We developed a new Javascript 3D network viewer JANet that provides the proposed visualisation strategies and has a customised interface for AgeFactDB data. It enables the analysis of gene lists in combination with AgeFactDB data and the interactive visualisation of the results. Conclusion Interactive 3D network visualisation allows to supplement complex database queries by a visually guided exploration process. The JANet interface allows gaining deeper insights into lifespan data patterns not accessible by common database queries alone. These concepts can be utilised in many other research fields. UR - https://doi.org/10.1186/s12859-018-2393-x KW - lifespan KW - ageing KW - gene network KW - 3D visualization KW - ageing factor database KW - AgeFactDB KW - differentially expressed genes Y1 - 2018 UR - https://doi.org/10.1186/s12859-018-2393-x UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27327 SN - 1471-2105 VL - 19 (2018) PB - BioMed Central CY - London ER - TY - JOUR A1 - Lausser, Ludwig A1 - Schäfer, Lisa M. A1 - Kestler, Hans A. T1 - Ordinal Classifiers Can Fail on Repetitive Class Structures JF - Archives of Data Science, Series A N2 - Ordinal classifiers are constrained classification algorithms that assume a predefined (total) order of the class labels to be reflected in the feature space of a dataset. This information is used to guide the training of ordinal classifiers and might lead to an improved classification performance. Incorrect assumptions on the order of a dataset can result in diminished detection rates. Ordinal classifiers can, therefore, be used to screen for ordinal class structures within a feature representation. While it was shown that algorithms could in principle reject incorrect class orderings, it is unclear if all remaining candidate orders reflect real ordinal structures in feature space. In this work we characterize the decision regions induced by ordinal classifiers. We show that they can fulfill different criteria that might be considered as ordinal reflections. These criteria are mainly determined by the connectedness and the neighborhood of the decision regions. We evaluate them for ordinal classifier cascades constructed from binary classifiers. We show that depending on the type of base classifier they bear the risk of not rejecting non ordinal, like partial repetitive, structures. UR - https://doi.org/10.5445/KSP/1000085951/25 Y1 - 2018 UR - https://doi.org/10.5445/KSP/1000085951/25 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27335 SN - 2363-9881 VL - 4 (2018) IS - 1 PB - KIT Scientific Publishing CY - Karlsruhe ER - TY - JOUR A1 - Lausser, Ludwig A1 - Schäfer, Lisa M. A1 - Schirra, Lyn-Rouven A1 - Szekely, Robin A1 - Schmid, Florian A1 - Kestler, Hans A. T1 - Assessing phenotype order in molecular data JF - Scientific Reports N2 - Biological entities are key elements of biomedical research. Their definition and their relationships are important in areas such as phylogenetic reconstruction, developmental processes or tumor evolution. Hypotheses about relationships like phenotype order are often postulated based on prior knowledge or belief. Evidence on a molecular level is typically unknown and whether total orders are reflected in the molecular measurements is unclear or not assessed. In this work we propose a method that allows a fast and exhaustive screening for total orders in large datasets. We utilise ordinal classifier cascades to identify discriminable molecular representations of the phenotypes. These classifiers are constrained by an order hypothesis and are highly sensitive to incorrect assumptions. Two new error bounds, which are introduced and theoretically proven, lead to a substantial speed-up and allow the application to large collections of many phenotypes. In our experiments we show that by exhaustively evaluating all possible candidate orders, we are able to identify phenotype orders that best coincide with the high-dimensional molecular profiles. UR - https://doi.org/10.1038/s41598-019-48150-z KW - machine learning KW - systems biology Y1 - 2019 UR - https://doi.org/10.1038/s41598-019-48150-z UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27353 SN - 2045-2322 VL - 9 PB - Springer Nature CY - London ER - TY - JOUR A1 - Lausser, Ludwig A1 - Szekely, Robin A1 - Klimmek, Attila A1 - Schmid, Florian A1 - Kestler, Hans A. T1 - Constraining classifiers in molecular analysis BT - invariance and robustness JF - Journal of the Royal Society Interface N2 - Analysing molecular profiles requires the selection of classification models that can cope with the high dimensionality and variability of these data. Also, improper reference point choice and scaling pose additional challenges. Often model selection is somewhat guided by ad hoc simulations rather than by sophisticated considerations on the properties of a categorization model. Here, we derive and report four linked linear concept classes/models with distinct invariance properties for high-dimensional molecular classification. We can further show that these concept classes also form a half-order of complexity classes in terms of Vapnik–Chervonenkis dimensions, which also implies increased generalization abilities. We implemented support vector machines with these properties. Surprisingly, we were able to attain comparable or even superior generalization abilities to the standard linear one on the 27 investigated RNA-Seq and microarray datasets. Our results indicate that a priori chosen invariant models can replace ad hoc robustness analysis by interpretable and theoretically guaranteed properties in molecular categorization. UR - https://doi.org/10.1098/rsif.2019.0612 KW - computational learning theory KW - classification KW - invariances KW - molecular profiles Y1 - 2020 UR - https://doi.org/10.1098/rsif.2019.0612 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27361 SN - 1742-5662 VL - 17 (2020) IS - 163 PB - The Royal Society CY - London ER - TY - JOUR A1 - Bellmann, Peter A1 - Lausser, Ludwig A1 - Kestler, Hans A. A1 - Schwenker, Friedhelm T1 - A Theoretical Approach to Ordinal Classification BT - Feature Space-Based Definition and Classifier-Independent Detection of Ordinal Class Structures JF - Applied Sciences N2 - Ordinal classification (OC) is a sub-discipline of multi-class classification (i.e., including at least three classes), in which the classes constitute an ordinal structure. Applications of ordinal classification can be found, for instance, in the medical field, e.g., with the class labels order, early stage-intermediate stage-final stage, corresponding to the task of classifying different stages of a certain disease. While the field of OC was continuously enhanced, e.g., by designing and adapting appropriate classification models as well as performance metrics, there is still a lack of a common mathematical definition for OC tasks. More precisely, in general, a classification task is defined as an OC task, solely based on the corresponding class label names. However, an ordinal class structure that is identified based on the class labels is not necessarily reflected in the corresponding feature space. In contrast, naturally any kind of multi-class classification task can consist of a set of arbitrary class labels that form an ordinal structure which can be observed in the current feature space. Based on this simple observation, in this work, we present our generalised approach towards an intuitive working definition for OC tasks, which is based on the corresponding feature space and allows a classifier-independent detection of ordinal class structures. To this end, we introduce and discuss novel, OC-specific theoretical concepts. Moreover, we validate our proposed working definition in combination with a set of traditionally ordinal and traditionally non-ordinal data sets, and provide the results of the corresponding detection algorithm. Additionally, we motivate our theoretical concepts, based on an illustrative evaluation of one of the oldest and most popular machine learning data sets, i.e., on the traditionally non-ordinal Fisher’s Iris data set. UR - https://doi.org/10.3390/app12041815 KW - ordinal classification KW - detection of ordinal class structures KW - Fisher’s discriminant ratio Y1 - 2022 UR - https://doi.org/10.3390/app12041815 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27186 SN - 2076-3417 N1 - This article belongs to the Section "Computing and Artificial Intelligence" VL - 12 (2022) IS - 4 PB - MDPI CY - Basel ER - TY - JOUR A1 - Weidner, Felix M. A1 - Schwab, Julian D. A1 - Werle, Silke D. A1 - Ikonomi, Nensi A1 - Lausser, Ludwig A1 - Kestler, Hans A. T1 - Capturing dynamic relevance in Boolean networks using graph theoretical measures JF - Bioinformatics N2 - Motivation Interaction graphs are able to describe regulatory dependencies between compounds without capturing dynamics. In contrast, mathematical models that are based on interaction graphs allow to investigate the dynamics of biological systems. However, since dynamic complexity of these models grows exponentially with their size, exhaustive analyses of the dynamics and consequently screening all possible interventions eventually becomes infeasible. Thus, we designed an approach to identify dynamically relevant compounds based on the static network topology. Results Here, we present a method only based on static properties to identify dynamically influencing nodes. Coupling vertex betweenness and determinative power, we could capture relevant nodes for changing dynamics with an accuracy of 75% in a set of 35 published logical models. Further analyses of the selected compounds’ connectivity unravelled a new class of not highly connected nodes with high impact on the networks’ dynamics, which we call gatekeepers. We validated our method’s working concept on logical models, which can be readily scaled up to complex interaction networks, where dynamic analyses are not even feasible. UR - https://doi.org/10.1093/bioinformatics/btab277 Y1 - 2021 UR - https://doi.org/10.1093/bioinformatics/btab277 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27194 SN - 1460-2059 SN - 1367-4803 N1 - Supplementary data are available at Bioinformatics online. VL - 37 IS - 20 SP - 3530 EP - 3537 PB - Oxford University Press CY - Oxford ER - TY - JOUR A1 - Anderl-Straub, Sarah A1 - Lausser, Ludwig A1 - Lombardi, Jolina A1 - Uttner, Ingo A1 - Fassbender, Klaus A1 - Fliessbach, Klaus A1 - Huppertz, Hans-Jürgen A1 - Jahn, Holger A1 - Kornhuber, Johannes A1 - Obrig, Hellmuth A1 - Schneider, Anja A1 - Semler, Elisa A1 - Synofzik, Matthis A1 - Danek, Adrian A1 - Prudlo, Johannes A1 - Kassubek, Jan A1 - Landwehrmeyer, Bernhard A1 - Lauer, Martin A1 - Volk, Alexander E. A1 - Wiltfang, Jens A1 - Diehl-Schmid, Janine A1 - Ludolph, Albert C. A1 - Schroeter, Matthias A1 - Kestler, Hans A. A1 - Otto, Markus T1 - Predicting disease progression in behavioral variant frontotemporal dementia JF - Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring N2 - Introduction The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive value of individual decline. Methods One hundred five bvFTD patients were recruited from the German frontotemporal lobar degeneration (FTLD) consortium study. After defining two groups (“fast progressors” vs. “slow progressors”), we investigated the predictive value of MR brain volumes for disease progression rates performing exhaustive screenings with multivariate classification models. Results We identified areas that predict disease progression rate within 1 year. Prediction measures revealed an overall accuracy of 80% across our 50 top classification models. Especially the pallidum, middle temporal gyrus, inferior frontal gyrus, cingulate gyrus, middle orbitofrontal gyrus, and insula occurred in these models. Discussion Based on the revealed marker combinations an individual prognosis seems to be feasible. This might be used in clinical studies on an individualized progression model. UR - https://doi.org/10.1002/dad2.12262 KW - behavioral variant frontotemporal dementia KW - brain volume KW - classification models KW - disease progression KW - frontotemporal dementia KW - prognosis Y1 - 2021 UR - https://doi.org/10.1002/dad2.12262 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27212 SN - 2352-8729 N1 - Collaborators FTLD consortium: Christine v. Arnim, Petra Steinacker, Georg Grön, Manuel Maler, Maryna Polyakova, Christopher Weise, Lea Hüper, Daniele Pino, Annerose Engel, Johannes Levin, Catharina Prix, Elisabeth Wlasich, Lina Riedl, Felix Müller-Sarnowski, Carola Roßmeier, Felix Oberhauser, Kai Schumacher, Jan Lehmbeck, Thomas Duning, Andreas Johnen. VL - 13 IS - 1 PB - Alzheimer’s Association CY - Chicago ER - TY - JOUR A1 - Niederschweiberer, Moritz A1 - Schaefer, Patrick M. A1 - Singh, Larry N. A1 - Lausser, Ludwig A1 - Bhosale, Devyani A1 - Hesse, Raphael A1 - Calzia, Enrico A1 - Kestler, Hans A. A1 - Rueck, Angelika A1 - Wallace, Douglas A1 - Einem, Bjoern von A1 - Arnim, Christine A. F. von T1 - NADH Fluorescence Lifetime Imaging Microscopy Reveals Selective Mitochondrial Dysfunction in Neurons Overexpressing Alzheimer’s Disease–Related Proteins JF - Frontiers in Molecular Biosciences N2 - Alzheimer’s disease (AD), the most prevalent form of dementia, affects globally more than 30 million people suffering from cognitive deficits and neuropsychiatric symptoms. Substantial evidence for the involvement of mitochondrial dysfunction in the development and/or progression of AD has been shown in addition to the pathological hallmarks amyloid beta (Aβ) and tau. Still, the selective vulnerability and associated selective mitochondrial dysfunction cannot even be resolved to date. We aimed at optically quantifying mitochondrial function on a single-cell level in primary hippocampal neuron models of AD, unraveling differential involvement of cell and mitochondrial populations in amyloid precursor protein (APP)-associated mitochondrial dysfunction. NADH lifetime imaging is a highly sensitive marker-free method with high spatial resolution. However, deciphering cellular bioenergetics of complex cells like primary neurons has still not succeeded yet. To achieve this, we combined highly sensitive NADH lifetime imaging with respiratory inhibitor treatment, allowing characterization of mitochondrial function down to even the subcellular level in primary neurons. Measuring NADH lifetime of the same neuron before and after respiratory treatment reveals the metabolic delta, which can be taken as a surrogate for cellular redox capacity. Correlating NADH lifetime delta with overexpression strength of Aβ-related proteins on the single-cell level, we could verify the important role of intracellular Aβ-mediated mitochondrial toxicity. Subcellularly, we could demonstrate a higher respiration in neuronal somata in general than dendrites, but a similar impairment of somatic and dendritic mitochondria in our AD models. This illustrates the power of NADH lifetime imaging in revealing mitochondrial function on a single and even subcellular level and its potential to shed light into bioenergetic alterations in neuropsychiatric diseases and beyond. UR - https://doi.org/10.3389/fmolb.2021.671274 KW - mitochondria KW - energy metabolism KW - Alzheimer’s disease KW - NADH KW - redox imaging KW - amyloid beta Y1 - 2021 UR - https://doi.org/10.3389/fmolb.2021.671274 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27224 SN - 2296-889X N1 - Section: Molecular Diagnostics and Therapeutics VL - 8 PB - Frontiers CY - Lausanne ER - TY - JOUR A1 - Kraus, Johann M. A1 - Lausser, Ludwig A1 - Kuhn, Peter A1 - Jobst, Franz A1 - Bock, Michaela A1 - Halanke, Carolin A1 - Hummel, Michael A1 - Heuschmann, Peter A1 - Kestler, Hans A. T1 - Big data and precision medicine BT - challenges and strategies with healthcare data JF - International Journal of Data Science and Analytics UR - https://doi.org/10.1007/s41060-018-0095-0 KW - big data KW - precision medicine KW - health care KW - data science Y1 - 2018 UR - https://doi.org/10.1007/s41060-018-0095-0 SN - 2364-4168 SN - 2364-415X VL - 6 (2018) IS - 3 SP - 241 EP - 249 PB - Springer Nature CY - Cham ER - TY - JOUR A1 - Hesse, Raphael A1 - Einem, Bjoern von A1 - Wagner, Franziska A1 - Bott, Patricia A1 - Schwanzar, Daniel A1 - Jackson, Rosemary A1 - Föhr, Karl Josef A1 - Lausser, Ludwig A1 - Kroker, Katja S. A1 - Proepper, Christian A1 - Walther, Paul A1 - Kestler, Hans A. A1 - Spires-Jones, Tara A1 - Boeckers, Tobias A1 - Rosenbrock, Holger A1 - Arnim, Christine A. F. von T1 - sAPPβ and sAPPα increase structural complexity and E/I input ratio in primary hippocampal neurons and alter Ca2+ homeostasis and CREB1-signaling JF - Experimental Neurology UR - https://doi.org/10.1016/j.expneurol.2018.02.010 KW - sAPP KW - AD KW - neuronal plasticity KW - neurodevelopment KW - CREB-1 signaling Y1 - 2018 UR - https://doi.org/10.1016/j.expneurol.2018.02.010 SN - 0014-4886 VL - (2018) IS - 304 SP - 1 EP - 13 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Lausser, Ludwig A1 - Szekely, Robin A1 - Kestler, Hans A. T1 - Chained correlations for feature selection JF - Advances in Data Analysis and Classification N2 - Data-driven algorithms stand and fall with the availability and quality of existing data sources. Both can be limited in high-dimensional settings (n≫m). For example, supervised learning algorithms designed for molecular pheno- or genotyping are restricted to samples of the corresponding diagnostic classes. Samples of other related entities, such as arise in differential diagnosis, are usually not utilized in this learning scheme. Nevertheless, they might provide domain knowledge on the background or context of the original diagnostic task. In this work, we discuss the possibility of incorporating samples of foreign classes in the training of diagnostic classification models that can be related to the task of differential diagnosis. Especially in heterogeneous data collections comprising multiple diagnostic categories, the foreign ones can change the magnitude of available samples. More precisely, we utilize this information for the internal feature selection process of diagnostic models. We propose the use of chained correlations of original and foreign diagnostic classes. This method allows the detection of intermediate foreign classes by evaluating the correlation between class labels and features for each pair of original and foreign categories. Interestingly, this criterion does not require direct comparisons of the initial diagnostic groups and therefore, might be suitable for settings with restricted data access. KW - classification KW - feature selection KW - high-dimensional data KW - differential diagnosis Y1 - 2020 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27534 SN - 1862-5355 VL - 14 IS - 4 SP - 871 EP - 884 PB - Springer CY - Berlin ER - TY - JOUR A1 - Lausser, Ludwig A1 - Schäfer, Lisa M. A1 - Werle, Silke D. A1 - Kestler, Angelika M. R. A1 - Kestler, Hans A. T1 - Detecting Ordinal Subcascades JF - Neural Processing Letters N2 - Ordinal classifier cascades are constrained by a hypothesised order of the semantic class labels of a dataset. This order determines the overall structure of the decision regions in feature space. Assuming the correct order on these class labels will allow a high generalisation performance, while an incorrect one will lead to diminished results. In this way ordinal classifier systems can facilitate explorative data analysis allowing to screen for potential candidate orders of the class labels. Previously, we have shown that screening is possible for total orders of all class labels. However, as datasets might comprise samples of ordinal as well as non-ordinal classes, the assumption of a total ordering might be not appropriate. An analysis of subsets of classes is required to detect such hidden ordinal substructures. In this work, we devise a novel screening procedure for exhaustive evaluations of all order permutations of all subsets of classes by bounding the number of enumerations we have to examine. Experiments with multi-class data from diverse applications revealed ordinal substructures that generate new and support known relations. UR - https://doi.org/10.1007/s11063-020-10362-0 KW - ordinal classification KW - classifier cascades KW - error bounds KW - subsets KW - supersets Y1 - 2020 UR - https://doi.org/10.1007/s11063-020-10362-0 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27555 SN - 1573-773X SN - 1370-4621 VL - 52 IS - 3 SP - 2583 EP - 2605 PB - Springer Science+Business Media CY - Dordrecht ER - TY - JOUR A1 - Lausser, Ludwig A1 - Szekely, Robin A1 - Schmid, Florian A1 - Maucher, Markus A1 - Kestler, Hans A. T1 - Efficient cross-validation traversals in feature subset selection JF - Scientific Reports N2 - Sparse and robust classification models have the potential for revealing common predictive patterns that not only allow for categorizing objects into classes but also for generating mechanistic hypotheses. Identifying a small and informative subset of features is their main ingredient. However, the exponential search space of feature subsets and the heuristic nature of selection algorithms limit the coverage of these analyses, even for low-dimensional datasets. We present methods for reducing the computational complexity of feature selection criteria allowing for higher efficiency and coverage of screenings. We achieve this by reducing the preparation costs of high-dimensional subsets O(nm2) to those of one-dimensional ones O(m2). Our methods are based on a tight interaction between a parallelizable cross-validation traversal strategy and distance-based classification algorithms and can be used with any product distance or kernel. We evaluate the traversal strategy exemplarily in exhaustive feature subset selection experiments (perfect coverage). Its runtime, fitness landscape, and predictive performance are analyzed on publicly available datasets. Even in low-dimensional settings, we achieve approximately a 15-fold increase in exhaustively generating distance matrices for feature combinations bringing a new level of evaluations into reach. UR - https://doi.org/10.1038/s41598-022-25942-4 Y1 - 2022 UR - https://doi.org/10.1038/s41598-022-25942-4 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-30299 SN - 2045-2322 VL - 12 PB - Springer Nature CY - London ER - TY - JOUR A1 - Werle, Silke D. A1 - Ikonomi, Nensi A1 - Lausser, Ludwig A1 - Kestler, Annika M. T. U. A1 - Weidner, Felix M. A1 - Schwab, Julian D. A1 - Maier, Julia A1 - Buchholz, Malte A1 - Gress, Thomas M. A1 - Kestler, Angelika M. R. A1 - Kestler, Hans A. T1 - A systems biology approach to define mechanisms, phenotypes, and drivers in PanNETs with a personalized perspective JF - npj Systems Biology and Applications N2 - AbstractPancreatic neuroendocrine tumors (PanNETs) are a rare tumor entity with largely unpredictable progression and increasing incidence in developed countries. Molecular pathways involved in PanNETs development are still not elucidated, and specific biomarkers are missing. Moreover, the heterogeneity of PanNETs makes their treatment challenging and most approved targeted therapeutic options for PanNETs lack objective responses. Here, we applied a systems biology approach integrating dynamic modeling strategies, foreign classifier tailored approaches, and patient expression profiles to predict PanNETs progression as well as resistance mechanisms to clinically approved treatments such as the mammalian target of rapamycin complex 1 (mTORC1) inhibitors. We set up a model able to represent frequently reported PanNETs drivers in patient cohorts, such as Menin-1 (MEN1), Death domain associated protein (DAXX), Tuberous Sclerosis (TSC), as well as wild-type tumors. Model-based simulations suggested drivers of cancer progression as both first and second hits after MEN1 loss. In addition, we could predict the benefit of mTORC1 inhibitors on differentially mutated cohorts and hypothesize resistance mechanisms. Our approach sheds light on a more personalized prediction and treatment of PanNET mutant phenotypes. UR - https://doi.org/10.1038/s41540-023-00283-8 Y1 - 2023 UR - https://doi.org/10.1038/s41540-023-00283-8 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-38071 SN - 2056-7189 VL - 9 IS - 1 PB - Springer CY - Heidelberg ER - TY - CHAP A1 - Krüger, Max A1 - Lausser, Ludwig T1 - Identifying Key Features in Aggregated Real-Life AIS-Messages for Classification of Maritime Vessels by Random Forests T2 - 2023 26th International Conference on Information Fusion (FUSION) UR - https://doi.org/10.23919/FUSION52260.2023.10224109 KW - Classification KW - key features KW - AIS KW - aggregated messages KW - performance evaluation KW - experimental comparison KW - maritime surveillance systems Y1 - 2023 UR - https://doi.org/10.23919/FUSION52260.2023.10224109 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Lausser, Ludwig A1 - Szekely, Robin A1 - Kessler, Viktor A1 - Schwenker, Friedhelm ED - Pancioni, Luca ED - Schwenker, Friedhelm ED - Trentin, Edmondo T1 - Selecting Features from Foreign Classes T2 - Artificial Neural Networks in Pattern Recognition: 8th IAPR TC3 Workshop, ANNPR 2018, Siena, Italy, September 19–21, 2018: Proceedings UR - https://doi.org/10.1007/978-3-319-99978-4_5 Y1 - 2018 UR - https://doi.org/10.1007/978-3-319-99978-4_5 SN - 978-3-319-99978-4 SN - 978-3-319-99977-7 SP - 66 EP - 77 PB - Springer CY - Cham ER - TY - CHAP A1 - Bellmann, Peter A1 - Lausser, Ludwig A1 - Kestler, Hans A. ED - Torsello, Andrea ED - Rossi, Luca ED - Pelillo, Marcello ED - Biggio, Battista ED - Robles-Kelly, Antonio T1 - Experimental Analysis of Bidirectional Pairwise Ordinal Classifier Cascades T2 - Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshops, S+SSPR 2020, Padua, Italy, January 21–22, 2021: Proceedings UR - https://doi.org/10.1007/978-3-030-73973-7_6 KW - Ordinal classification KW - Ordinal classifier cascades Y1 - 2021 UR - https://doi.org/10.1007/978-3-030-73973-7_6 SN - 978-3-030-73973-7 SN - 978-3-030-73972-0 SP - 55 EP - 64 PB - Springer CY - Cham ER - TY - CHAP A1 - Bellmann, Peter A1 - Lausser, Ludwig A1 - Kestler, Hans A. A1 - Schwenker, Friedhelm ED - Del Bimbo, Alberto ED - Cucchiara, Rita ED - Sclaroff, Stan ED - Farinella, Giovanni Maria ED - Mei, Tao ED - Bertini, Marco ED - Escalante, Hugo Jair ED - Vezzani, Roberto T1 - Introducing Bidirectional Ordinal Classifier Cascades Based on a Pain Intensity Recognition Scenario T2 - Pattern Recognition: ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021: Proceedings, Part VI UR - https://doi.org/10.1007/978-3-030-68780-9_58 KW - Ordinal classifier cascades KW - Ordinal classification KW - Pain intensity recognition KW - Physiological signals Y1 - 2021 UR - https://doi.org/10.1007/978-3-030-68780-9_58 SN - 978-3-030-68780-9 SN - 978-3-030-68779-3 SP - 773 EP - 787 PB - Springer CY - Cham ER - TY - JOUR A1 - Thiam, Patrick A1 - Lausser, Ludwig A1 - Kloth, Christopher A1 - Blaich, Daniel A1 - Liebold, Andreas A1 - Beer, Meinrad A1 - Kestler, Hans A. T1 - Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images JF - Frontiers in Artificial Intelligence N2 - In recent years, several deep learning approaches have been successfully applied in the field of medical image analysis. More specifically, different deep neural network architectures have been proposed and assessed for the detection of various pathologies based on chest X-ray images. While the performed assessments have shown very promising results, most of them consist in training and evaluating the performance of the proposed approaches on a single data set. However, the generalization of such models is quite limited in a cross-domain setting, since a significant performance degradation can be observed when these models are evaluated on data sets stemming from different medical centers or recorded under different protocols. The performance degradation is mostly caused by the domain shift between the training set and the evaluation set. To alleviate this problem, different unsupervised domain adaptation approaches are proposed and evaluated in the current work, for the detection of cardiomegaly based on chest X-ray images, in a cross-domain setting. The proposed approaches generate domain invariant feature representations by adapting the parameters of a model optimized on a large set of labeled samples, to a set of unlabeled images stemming from a different data set. The performed evaluation points to the effectiveness of the proposed approaches, since the adapted models outperform optimized models which are directly applied to the evaluation sets without any form of domain adaptation. UR - https://doi.org/10.3389/frai.2023.1056422 KW - chest X-ray KW - cardiomegaly KW - deep learning KW - transfer learning KW - unsupervised domain adaptation Y1 - 2023 UR - https://doi.org/10.3389/frai.2023.1056422 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-32108 SN - 2624-8212 VL - 6 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Weidner, Felix M. A1 - Schwab, Julian D. A1 - Werle, Silke D. A1 - Ikonomi, Nensi A1 - Lausser, Ludwig A1 - Kestler, Hans A. T1 - Response to the letter to the editor: On the feasibility of dynamical analysis of network models of biochemical regulation JF - Bioinformatics UR - https://doi.org/10.1093/bioinformatics/btac318 Y1 - 2022 UR - https://doi.org/10.1093/bioinformatics/btac318 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-40238 SN - 1367-4811 VL - 38 IS - 14 SP - 3676 PB - Oxford University Press CY - Oxford ER - TY - JOUR A1 - Seufferlein, Thomas A1 - Lausser, Ludwig A1 - Stein, Alexander A1 - Arnold, Dirk A1 - Prager, Gerald A1 - Kasper-Virchow, Stefan A1 - Niedermeier, Michael A1 - Müller, Lothar A1 - Kubicka, Stefan A1 - König, Alexander A1 - Büchner-Steudel, Petra A1 - Wille, Kai A1 - Berger, Andreas W. A1 - Kestler, Angelika M. R. A1 - Kraus, Johann M. A1 - Werle, Silke D. A1 - Perkhofer, Lukas A1 - Ettrich, Thomas J. A1 - Kestler, Hans A. T1 - Prediction of resistance to bevacizumab plus FOLFOX in metastatic colorectal cancer—Results of the prospective multicenter PERMAD trial JF - PLOS ONE N2 - Background Anti-vascular endothelial growth factor (VEGF) monoclonal antibodies (mAbs) are widely used for tumor treatment, including metastatic colorectal cancer (mCRC). So far, there are no biomarkers that reliably predict resistance to anti-VEGF mAbs like bevacizumab. A biomarker-guided strategy for early and accurate assessment of resistance could avoid the use of non-effective treatment and improve patient outcomes. We hypothesized that repeated analysis of multiple cytokines and angiogenic growth factors (CAFs) before and during treatment using machine learning could provide an accurate and earlier, i.e., 100 days before conventional radiologic staging, prediction of resistance to first-line mCRC treatment with FOLFOX plus bevacizumab. Patients and methods 15 German and Austrian centers prospectively recruited 50 mCRC patients receiving FOLFOX plus bevacizumab as first-line treatment. Plasma samples were collected every two weeks until radiologic progression (RECIST 1.1) as determined by CT scans performed every 2 months. 102 pre-selected CAFs were centrally analyzed using a cytokine multiplex assay (Luminex, Myriad RBM). Results Using random forests, we developed a predictive machine learning model that discriminated between the situations of “no progress within 100 days before radiological progress” and “progress within 100 days before radiological progress”. We could further identify a combination of ten out of the 102 CAF markers, which fulfilled this task with 78.2% accuracy, 71.8% sensitivity, and 82.5% specificity. Conclusions We identified a CAF marker combination that indicates treatment resistance to FOLFOX plus bevacizumab in patients with mCRC within 100 days prior to radiologic progress. UR - https://doi.org/10.1371/journal.pone.0304324 Y1 - 2024 UR - https://doi.org/10.1371/journal.pone.0304324 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-49003 SN - 1932-6203 VL - 19 IS - 6 PB - PLOS CY - San Francisco ER - TY - JOUR A1 - Lausser, Ludwig A1 - Szekely, Robin A1 - Kestler, Hans A. T1 - Permutation-invariant linear classifiers JF - Machine Learning N2 - Invariant concept classes form the backbone of classification algorithms immune to specific data transformations, ensuring consistent predictions regardless of these alterations. However, this robustness can come at the cost of limited access to the original sample information, potentially impacting generalization performance. This study introduces an addition to these classes—the permutation-invariant linear classifiers. Distinguished by their structural characteristics, permutation-invariant linear classifiers are unaffected by permutations on feature vectors, a property not guaranteed by other non-constant linear classifiers. The study characterizes this new concept class, highlighting its constant capacity, independent of input dimensionality. In practical assessments using linear support vector machines, the permutation-invariant classifiers exhibit superior performance in permutation experiments on artificial datasets and real mutation profiles. Interestingly, they outperform general linear classifiers not only in permutation experiments but also in permutation-free settings, surpassing unconstrained counterparts. Additionally, findings from real mutation profiles support the significance of tumor mutational burden as a biomarker. UR - https://doi.org/10.1007/s10994-024-06561-8 Y1 - 2024 UR - https://doi.org/10.1007/s10994-024-06561-8 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-49116 SN - 1573-0565 VL - 113 IS - 10 SP - 7195 EP - 7221 PB - Springer CY - Dordrecht ER - TY - INPR A1 - Ganz, Jonathan A1 - Ammeling, Jonas A1 - Rosbach, Emely A1 - Lausser, Ludwig A1 - Bertram, Christof A1 - Breininger, Katharina A1 - Aubreville, Marc T1 - Is Self-Supervision Enough? Benchmarking Foundation Models Against End-to-End Training for Mitotic Figure Classification N2 - 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. UR - https://doi.org/10.48550/arXiv.2412.06365 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2412.06365 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-59672 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Lausser, Ludwig A1 - Bertram, Christof A1 - Klopfleisch, Robert A1 - Aubreville, Marc ED - Deserno, Thomas Martin ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Palm, Christoph ED - Tolxdorff, Thomas T1 - Limits of Human Expert Ensembles in Mitosis Multi-expert Ground Truth Generation T2 - Bildverarbeitung für die Medizin 2023: Proceedings, German Workshop on Medical Image Computing, Braunschweig, July 2-4, 2023 UR - https://doi.org/10.1007/978-3-658-41657-7_27 Y1 - 2023 UR - https://doi.org/10.1007/978-3-658-41657-7_27 SN - 978-3-658-41657-7 SN - 978-3-658-41656-0 SP - 116 EP - 121 PB - Springer Vieweg CY - Wiesbaden ER - TY - JOUR A1 - Kubis, Lea A1 - Schwab, Julian D. A1 - Werle, Silke D. A1 - Lausser, Ludwig A1 - Tümpel, Stefan A1 - Pfister, Astrid S. A1 - Kühl, Michael A1 - Kestler, Hans A. T1 - A Boolean network of the crosstalk between IGF and Wnt signaling in aging satellite cells JF - PLOS ONE N2 - Aging is a complex biological process, which determines the life span of an organism. Insulin-like growth factor (IGF) and Wnt signaling pathways govern the process of aging. Both pathways share common downstream targets that allow competitive crosstalk between these branches. Of note, a shift from IGF to Wnt signaling has been observed during aging of satellite cells. Biological regulatory networks necessary to recreate aging have not yet been discovered. Here, we established a mathematical in silico model that robustly recapitulates the crosstalk between IGF and Wnt signaling. Strikingly, it predicts critical nodes following a shift from IGF to Wnt signaling. These findings indicate that this shift might cause age-related diseases. UR - https://doi.org/10.1371/journal.pone.0195126 Y1 - 2018 UR - https://doi.org/10.1371/journal.pone.0195126 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-27280 SN - 1932-6203 VL - 13 IS - 3 PB - PLOS CY - San Francisco ER - TY - JOUR A1 - Ammeling, Jonas A1 - Ganz, Jonathan A1 - Rosbach, Emely A1 - Lausser, Ludwig A1 - Bertram, Christof A1 - Breininger, Katharina A1 - Aubreville, Marc T1 - Benchmarking Foundation Models for Mitotic Figure Classification JF - Machine Learning for Biomedical Imaging N2 - The performance of deep learning models is known to scale with data quantity and diversity. In pathology, as in many other medical imaging domains, the availability of labeled images for a specific task is often limited. Self-supervised learning techniques have enabled the use of vast amounts of unlabeled data to train large-scale neural networks, i.e., foundation models, that can address the limited data problem by providing semantically rich feature vectors that can generalize well to new tasks with minimal training effort increasing model performance and robustness. In this work, we investigate the use of foundation models for mitotic figure classification. The mitotic count, which can be derived from this classification task, is an independent prognostic marker for specific tumors and part of certain tumor grading systems. In particular, we investigate the data scaling laws on multiple current foundation models and evaluate their robustness to unseen tumor domains. Next to the commonly used linear probing paradigm, we also adapt the models using low-rank adaptation (LoRA) of their attention mechanisms. We compare all models against end-to-end-trained baselines, both CNNs and Vision Transformers. Our results demonstrate that LoRA-adapted foundation models provide superior performance to those adapted with standard linear probing, reaching performance levels close to 100 % data availability with only 10 % of training data. Furthermore, LoRA-adaptation of the most recent foundation models almost closes the out-of-domain performance gap when evaluated on unseen tumor domains. However, full fine-tuning of traditional architectures still yields competitive performance. UR - https://doi.org/10.59275/j.melba.2026-a3eb Y1 - 2026 UR - https://doi.org/10.59275/j.melba.2026-a3eb UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-66220 SN - 2766-905X VL - 3 IS - MELBA–BVM 2025 Special Issue SP - 38 EP - 55 PB - Melba editors CY - [s. l.] ER - TY - CHAP A1 - Ganz, Jonathan A1 - Ammeling, Jonas A1 - Rosbach, Emely A1 - Lausser, Ludwig A1 - Bertram, Christof A1 - Breininger, Katharina A1 - Aubreville, Marc ED - Palm, Christoph ED - Breininger, Katharina ED - Deserno, Thomas Martin ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Tolxdorff, Thomas T1 - Is Self-supervision Enough? BT - Benchmarking Foundation Models Against End-to-end Training for Mitotic Figure Classification T2 - Bildverarbeitung für die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09–11, 2025 UR - https://doi.org/10.1007/978-3-658-47422-5_15 Y1 - 2025 UR - https://doi.org/10.1007/978-3-658-47422-5_15 SN - 978-3-658-47422-5 SP - 63 EP - 68 PB - Springer Vieweg CY - Wiesbaden ER - TY - INPR A1 - Ammeling, Jonas A1 - Ganz, Jonathan A1 - Rosbach, Emely A1 - Lausser, Ludwig A1 - Bertram, Christof A1 - Breininger, Katharina A1 - Aubreville, Marc T1 - Benchmarking Foundation Models for Mitotic Figure Classification UR - https://doi.org/10.48550/arXiv.2508.04441 Y1 - 2025 UR - https://doi.org/10.48550/arXiv.2508.04441 PB - arXiv CY - Ithaca ER -