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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Scheible, Raphael Frei, Johann Thomczyk, Fabian He, Henry Tippmann, Patric Knaus, Jochen Jaravine, Victor Kramer, Frank Boeker, Martin Institute for AI and Informatics in Medicine University Hospital rechts der Isar Technical University Munich Germany IT-Infrastructure for Translational Medical Research Faculty of Applied Computer Science University of Augsburg Germany Data Integration Center Faculty of Medicine University of Freiburg Germany School of Computation Information and Technology Technical University Munich Germany Institute of Medical Biometry and Statistics Medical Center Faculty of Medicine University of Freiburg Germany Freiburg Center for Data Analysis and Modeling University of Freiburg Germany Hengrui Europe Biosciences Zurich Switzerland

ISBN: (纸本)9798891761643

Pre-trained language models have significantly advanced natural language processing (NLP), especially with the introduction of BERT and its optimized version, RoBERTa. While initial research focused on English, single-language models can be advantageous compared to multilingual ones in terms of pre-training effort, overall resource efficiency or downstream task performance. Despite the growing popularity of prompt-based LLMs, more compute-efficient BERT-like models remain highly relevant. In this work, we present the first German single-language RoBERTa model, GottBERT, pre-trained exclusively on the German portion of the OSCAR dataset. Additionally, we investigated the impact of filtering the OSCAR corpus. GottBERT was pre-trained using fairseq and standard hyperparameters. We evaluated its performance on two Named Entity Recognition (NER) tasks (Conll 2003 and GermEval 2014) and three text classification tasks (GermEval 2018 fine and coarse, and 10kGNAD) against existing German BERT models and two multilingual models. Performance was measured using the F1 score and accuracy. The GottBERT base and large models showed competitive performance, with GottBERT leading among the base models in 4 of 6 tasks. Contrary to our expectation, the applied filtering did not significantly affect the results. To support the German NLP research community, we are releasing the GottBERT models under the MIT license. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Investigating a domain adaptation approach for integrating different measurement instruments in a longitudinal clinical registry

arXiv

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arXiv 2023年

作者： Hackenberg, Maren Pfaffenlehner, Michelle Behrens, Max Pechmann, Astrid Kirschner, Janbernd Binder, Harald Institute of Medical Biometry and Statistics Faculty of Medicine and Medical Center University of Freiburg Freiburg Germany Freiburg Center for Data Analysis and Modeling University of Freiburg Freiburg Germany Department of Neuropediatrics and Muscle Disorders Faculty of Medicine and Medical Center University of Freiburg Freiburg Germany University of Freiburg Freiburg Germany

In a longitudinal clinical registry, different measurement instruments might have been used for assessing individuals at different time points. To combine them, we investigate deep learning techniques for obtaining a joint latent representation, to which the items of different measurement instruments are mapped. This corresponds to domain adaptation, an established concept in computer science for image data. Using the proposed approach as an example, we evaluate the potential of domain adaptation in a longitudinal cohort setting with a rather small number of time points, motivated by an application with different motor function measurement instruments in a registry of spinal muscular atrophy (SMA) patients. There, we model trajectories in the latent representation by ordinary differential equations (ODEs), where person-specific ODE parameters are inferred from baseline characteristics. The goodness of fit and complexity of the ODE solutions then allows to judge the measurement instrument mappings. We subsequently explore how alignment can be improved by incorporating corresponding penalty terms into model fitting. To systematically investigate the effect of differences between measurement instruments, we consider several scenarios based on modified SMA data, including scenarios where a mapping should be feasible in principle and scenarios where no perfect mapping is available. While misalignment increases in more complex scenarios, some structure is still recovered, even if the availability of measurement instruments depends on patient state. A reasonable mapping is feasible also in the more complex real SMA dataset. These results indicate that domain adaptation might be more generally useful in statistical modeling for longitudinal registry data. © 2023, CC BY.

关键词： Ordinary differential equations

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COMBINING PROPENSITY SCORE METHODS WITH VARIATIONAL AUTOENCODERS FOR GENERATING SYNTHETIC data IN PRESENCE OF LATENT SUB-GROUPS

arXiv

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arXiv 2023年

作者： Farhadyar, Kiana Bonofiglio, Federico Hackenberg, Maren Zöller, Daniela Binder, Harald Institute of Medical Biometry and Statistics Faculty of Medicine and Medical Center Freiburg Center for Data Analysis and Modeling University of Freiburg Freiburg Germany National Research Council of Italy ISMAR La Spezia Italy Institute of Medical Biometry and Statistics Faculty of Medicine and Medical Center University of Freiburg Freiburg Germany

In settings requiring synthetic data generation based on a clinical cohort, e.g., due to data protection regulations, heterogeneity across individuals might be a nuisance that we need to control or faithfully preserve. The sources of such heterogeneity might be known, e.g., as indicated by sub-groups labels, or might be unknown and thus reflected only in properties of distributions, such as bimodality or skewness. We investigate how such heterogeneity can be preserved and controlled when obtaining synthetic data from variational autoencoders (VAEs), i.e., a generative deep learning technique that utilizes a low-dimensional latent representation. To faithfully reproduce unknown heterogeneity reflected in marginal distributions, we propose to combine VAEs with pre-transformations. For dealing with known heterogeneity due to sub-groups, we complement VAEs with models for group membership, specifically from propensity score regression. The evaluation is performed with a realistic simulation design that features sub-groups and challenging marginal distributions. The proposed approach faithfully recovers the latter, compared to synthetic data approaches that focus purely on marginal distributions. Propensity scores add complementary information, e.g., when visualized in the latent space, and enable sampling of synthetic data with or without sub-group specific characteristics. We also illustrate the proposed approach with real data from an international stroke trial that exhibits considerable distribution differences between study sites, in addition to bimodality. These results indicate that describing heterogeneity by statistical approaches, such as propensity score regression, might be more generally useful for complementing generative deep learning for obtaining synthetic data that faithfully reflects structure from clinical cohorts. © 2023, CC BY.

关键词： Deep learning

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Detecting frequency modulation in stochastic time-series data

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Physical Review E 2022年第2期106卷 024204-024204页

作者： Adrian L. Hauber Christian Sigloch Jens Timmer Institute of Physics University of Freiburg 79104 Freiburg Germany Freiburg Center for Data Analysis and Modeling (FDM) University of Freiburg 79104 Freiburg Germany Developmental Biology Institute Biology I Faculty of Biology University of Freiburg 79104 Freiburg Germany Spemann Graduate School of Biology and Medicine (SGBM) University of Freiburg 79104 Freiburg Germany Centre for Integrative Biological Signalling Studies (CIBSS) University of Freiburg 79104 Freiburg Germany

We propose a statistical test to identify nonstationary frequency-modulated stochastic processes from time-series data. Our method uses the instantaneous phase as a discriminatory statistics with reliable critical values derived from surrogate data. We simulated an oscillatory second-order autoregressive process to evaluate the size and power of the test. We found that the test we propose is able to correctly identify more than 99% of nonstationary data when the frequency of the simulated data is doubled after the first half of the time series. Our method is easily interpretable, computationally cheap, and does not require choosing hyperparameters that are dependent on the data.

关键词： Statistical methods Stochastic processes & statistics

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Improving prediction models by incorporating external data with weights based on similarity

arXiv

引用

arXiv 2024年

作者： Behrens, Max Farhadizadeh, Maryam Rohde, Angelika Rühle, Alexander Nicolay, Nils H. Binder, Harald Zöller, Daniela Institute of Medical Biometry and Statistics Faculty of Medicine and Medical Center University of Freiburg Stefan-Meier-Strase 26 Freiburg79104 Germany Freiburg Center for Data Analysis and Modeling University of Freiburg Ernst-Zermelo-Strase 1 Freiburg79104 Germany University of Freiburg Department of Mathematical Stochastics Ernst-Zermelo-Strase 1 Freiburg79104 Germany Department of Radiation Oncology University of Freiburg-Medical Center Robert-Koch-Strase 3 Freiburg79106 Germany Department of Radiation Oncology University of Leipzig Stephanstr. 9a Leipzig04103 Germany

In clinical settings, we often face the challenge of building prediction models based on small observational data sets. For example, such a data set might be from a medical center in a multicenter study. Differences between centers might be large, thus requiring specific models based on the data set from the target center. Still, we want to borrow information from the external centers, to deal with small sample sizes. There are approaches that either assign weights to each external data set or each external observation. To incorporate information on differences between data sets and observations, we propose an approach that combines both into weights that can be incorporated into a likelihood for fitting regression models. Specifically, we suggest weights at the data set level that incorporate information on how well the models that provide the observation weights distinguish between data sets. Technically, this takes the form of inverse probability weighting. We explore different scenarios where covariates and outcomes differ among data sets, informing our simulation design for method evaluation. The concept of effective sample size is used for understanding the effectiveness of our subgroup modeling approach. We demonstrate our approach through a clinical application, predicting applied radiotherapy doses for cancer patients. Generally, the proposed approach provides improved prediction performance when external data sets are similar. We thus provide a method for quantifying similarity of external data sets to the target data set and use this similarity to include external observations for improving performance in a target data set prediction modeling task with small data. © 2024, CC BY.

关键词： Forecasting

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TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI

arXiv

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arXiv 2024年

作者： D'Antonoli, Tugba Akinci Berger, Lucas K. Indrakanti, Ashraya K. Vishwanathan, Nathan Weiss, Jakob Jung, Matthias Berkarda, Zeynep Rau, Alexander Reisert, Marco Küstner, Thomas Walter, Alexandra Merkle, Elmar M. Boll, Daniel Breit, Hanns-Christian Nicoli, Andrew Phillip Segeroth, Martin Cyriac, Joshy Yang, Shan Wasserthal, Jakob Clinic of Radiology and Nuclear Medicine University Hospital Basel Petersgraben 4 BaselCH-4031 Switzerland Department of Diagnostic and Interventional Radiology Faculty of Medicine Medical Center University of Freiburg University of Freiburg Germany Department of Stereotactic and Functional Neurosurgery Faculty of Medicine Medical Center University of Freiburg University of Freiburg Freiburg Germany Medical Image and Data Analysis University Hospital of Tuebingen Tuebingen Germany Department of Biomedical Physics in Radiation Oncology German Cancer Research Center Heidelberg Germany Scientific Computing Center Karlsruhe Institute of Technology Karlsruhe Germany

Background Since the introduction of TotalSegmentator CT, there is demand for a similar robust automated MRI segmentation tool that can be applied across all MRI sequences and anatomic structures. Purpose To develop and evaluate an automated MRI segmentation model for robust segmentation of major anatomic structures independently of MRI sequence. Materials and Methods In this retrospective study, a nnU-Net model (TotalSegmentator) was trained on MRI and CT examinations to segment 80 anatomic structures relevant for use cases such as organ volumetry, disease characterization, surgical planning and opportunistic screening. Examinations were randomly sampled from routine clinical studies to represent real-world examples. Dice scores were calculated between the predicted segmentations and expert radiologist reference standard segmentations to evaluate model performance on an internal test set, two external test sets and against two publicly available models, and TotalSegmentator CT. The model was applied to an internal dataset containing abdominal MRIs to investigate age-dependent volume changes. Wilcoxon signed-rank test was used. Results A total of 1143 examinations (616 MRIs, 527 CTs) (median age 61 years, IQR 50-72) were split into training (n=1088, CT and MRI) and an internal test set (n=55;only MRI), two external test sets (AMOS, n=20;CHAOS, n=20;only MRI), and an internal aging-study dataset of 8672 abdominal MRIs (median age 59 years, IQR 45-70) were included. The model showed a Dice Score of 0.839 on the internal test set and outperformed two other models (Dice Score, 0.862 versus 0.759;and 0.838 versus 0.560;p Copyright © 2024, The Authors. All rights reserved.

关键词： Computerized tomography

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Adapting deep generative approaches for getting synthetic data with realistic marginal distributions

arXiv

引用

arXiv 2021年

作者： Farhadyar, Kiana Bonofiglio, Federico Zöller, Daniela Binder, Harald Institute of Medical Biometry and Statistics Faculty of Medicine and Medical Center University of Freiburg Freiburg79104 Germany Freiburg Center for Data Analysis and Modeling University of Freiburg Freiburg79104 Germany National Research Council of Italy Ismar Forte Santa Teresa Lerici - La Spezia19032 Italy

Synthetic data generation is of great interest in diverse applications, such as for privacy protection. Deep generative models, such as variational autoencoders (VAEs), are a popular approach for creating such synthetic datasets from original data. Despite the success of VAEs, there are limitations when it comes to the bimodal and skewed marginal distributions. These deviate from the unimodal symmetric distributions that are encouraged by the normality assumption typically used for the latent representations in VAEs. While there are extensions that assume other distributions for the latent space, this does not generally increase flexibility for data with many different distributions. Therefore, we propose a novel method, pre-transformation variational autoencoders (PTVAEs), to specifically address bimodal and skewed data, by employing pre-transformations at the level of original variables. Two types of transformations are used to bring the data close to a normal distribution by a separate parameter optimization for each variable in a dataset. We compare the performance of our method with other state-of-the-art methods for synthetic data generation. In addition to the visual comparison, we use a utility measurement for a quantitative evaluation. The results show that the PTVAE approach can outperform others in both bimodal and skewed data generation. Furthermore, the simplicity of the approach makes it usable in combination with other extensions of VAE. Copyright © 2021, The Authors. All rights reserved.

关键词： Metadata

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CSIG-11. TUMOR-CELL INTRINSIC CALCIUM OSCILLATIONS DRIVE BRAIN METASTASIS

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Neuro-Oncology 2024年第SUPPLEMENT_8期26卷 viii65–viii66页

作者： Hebach, Nils R Olshausen, Niels Schlag, Joana S Bang, Severin Kraft, Theresa Mayer, Chanté Linke, Zoe Westphal, Dana Seifert, Michael Timmer, Jens Wick, Wolfgang Winkler, Frank Karreman, Matthia A Clinical Cooperation Unit Neurooncology German Cancer Research Center Heidelberg Germany Neurology Clinic and National Center for Tumor Diseases Heidelberg Germany Institute of Physics University of Freiburg Freiburg Germany Freiburg Center for Data Analysis and Modeling (FDM) Freiburg Germany Institute for Medical Informatics and Biometry (IMB) Carl Gustav Carus Faculty of Medicine Dresden Germany National Center for Tumor Diseases (NCT) Dresden Germany Department of Dermatology Medical Faculty and University Hospital Carl Gustav Carus Dresden Germany

Calcium is a ubiquitous secondary messenger which regulates many cellular processes. Recently, we have shown that a subpopulation of glioblastoma cells can intrinsically generate calcium oscillations which drive proliferative signalling. Here, we set out to investigate whether such cancer cell autonomous oscillations are also present in secondary brain tumours. Employing longitudinal intravital two-photon microscopy in awake mice, we demonstrate that brain metastases of melanoma and lung cancer exhibit spontaneous calcium oscillationsin vivoalong the metastatic cascade. These oscillations are already observed during intravascular arrest and inin vitromonocultures, suggesting a tumour-cell intrinsic origin. We developed a fully automatic image analysis pipeline to accurately quantify calcium oscillations bothin vivoandin vitro. Mathematical modelling of cytosolic and ER calcium stores can predict stable calcium oscillations with similar frequency and peak characteristics as those experimentally observed. We discovered that these oscillations are tightly regulated by an interplay of calcium-induced calcium release and store-operated calcium entry (SOCE). Similarly, SOCE-related ion channels are enriched in tissue from human melanoma brain metastases compared to healthy brain controls and calcium activity can also be observedex vivoin patient-derived brain metastasis resections. Finally, by correlating calcium activity and EdU incorporation we discovered that the presence of calcium oscillations predicts their proliferative potentialin vitro. Importantly, by intravital two-photon microscopy we show that calcium activity correlates with metastasis size in preclinical models, suggesting a potential new therapeutic angle from which to target brain metastases.

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Non-parametric model-based estimation of the effective reproduction number for SARS-CoV-2

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AIP Conference Proceedings 2023年第1期2872卷

作者： Jacques Hermes Marcus Rosenblatt Christian Tönsing Jens Timmer Institute of Physics University of Freiburg Hermann-Herder-Str. 3 79104 Freiburg Germany Freiburg Center for Data Analysis and Modelling (FDM) University of Freiburg Ernst-Zermelo-Str. 1 79104 Freiburg Germany Centre for Integrative Biological Signalling Studies (CIBSS) University of Freiburg Schänzlestr. 18 79104 Freiburg Germany

Viral outbreaks, such as the COVID-19 pandemic, are commonly described by compartmental models by means of ordinary differential equation (ODE) systems. The parameter values of these ODE models are typically unknown and need to be estimated based on accessible data. In order to describe realistic pandemic scenarios with strongly varying situations, these model parameters need to be assumed as time-dependent. While parameter estimation for the typical case of time-constant parameters does not pose larger issues, the determination of time-dependent parameters, e.g. the transition rates of compartmental models, remains notoriously difficult, since the function class of these time-dependent parameters is ***, we propose a combination of an Augmented Kalman Smoother with an Expectation-Maximization algorithm to simultaneously estimate all time-dependent parameters in an SIRD compartmental model. The approach is applicable to general systems of ODEs with time-varying parameters. In particular, it does not require prior knowledge on model parameters or any further assumptions on the function class of the time-dependencies of the ODE. In contrast to other approaches, no assumptions on the parameterization of the serial interval distribution are required for the estimation of SIRD-model parameters. With the presented method, we are able to adequately describe time series of COVID-19 data in Germany including strong fluctuations and multiple waves, and to give non-parametric model-based time course estimates for the effective reproduction number.

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A systematic review of challenges and proposed solutions in modeling multimodal data

arXiv

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arXiv 2025年

作者： Farhadizadeh, Maryam Weymann, Maria Blaß, Michael Kraus, Johann Gundler, Christopher Walter, Sebastian Hempen, Noah Binder, Harald Binder, Nadine Institute of General Practice/Family Medicine Faculty of Medicine Medical Center University of Freiburg Elsässer Straße 2M Freiburg79110 Germany Freiburg Center for Data Analysis Modeling and AI University of Freiburg Ernst-Zermelo-Str. 1 Freiburg79104 Germany Institute of Medical Biometry and Statistics Faculty of Medicine Medical Center University of Freiburg Stefan-Meier-Straße 26 Freiburg79104 Germany Institute for Applied Medical Informatics University Medical Center Hamburg-Eppendorf Martinistraße 52 Hamburg20251 Germany Institute of Medical Systems Biology Ulm University Helmholtzstraße 16 Ulm89081 Germany Department of Computer Science Faculty of Engineering University of Freiburg Georges-Köhler-Allee 051 Freiburg79110 Germany

Multimodal data modeling has emerged as a powerful approach in clinical research, enabling the integration of diverse data types such as imaging, genomics, wearable sensors, and electronic health records. Despite its potential to improve diagnostic accuracy and support personalized care, modeling such heterogeneous data presents significant technical challenges. This systematic review synthesizes findings from 69 studies to identify common obstacles, including missing modalities, limited sample sizes, dimensionality imbalance, interpretability issues, and finding the optimal fusion techniques. We highlight recent methodological advances, such as transfer learning, generative models, attention mechanisms, and neural architecture search that offer promising solutions. By mapping current trends and innovations, this review provides a comprehensive overview of the field and offers practical insights to guide future research and development in multimodal modeling for medical applications. © 2025, CC BY.

关键词： Clinical research

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