Markus Reischl

• Prof. Dr.
• Karlsruhe Institute of Technology

With the increasing prevalence of artificial intelligence, careful evaluation of inherent biases needs to be conducted to form the basis for alleviating the effects these predispositions can have on users. Large language models (LLMs) are predominantly used by many as a primary source of information for various topics. LLMs frequently make factual...

Evaluating bias in Large Language Models (LLMs) has become a pivotal issue in current Artificial Intelligence (AI) research due to their significant impact on societal dynamics. Recognizing political bias in LLMs is particularly important as they approach performative prediction, influencing societal behavior and political events, such as the upcom...

Current state-of-the-art evaluation methods for 6D pose estimation have several significant drawbacks. Existing error metrics can produce near-zero errors for poor pose estimations and are heavily dependent on the object point cloud used, resulting in vastly different outcomes for different objects. Furthermore, false detections are not considered...

Biomedical research increasingly relies on three-dimensional (3D) cell culture models and artificial-intelligence-based analysis can potentially facilitate a detailed and accurate feature extraction on a single-cell level. However, this requires for a precise segmentation of 3D cell datasets, which in turn demands high-quality ground truth for trai...

Medical imaging scenarios are characterized by varying image modalities, several organs/cell shapes, and little annotated data because of the expertise required for labeling. The successful use of state-of-the-art deep-learning approaches requires a large amount of annotated data or a pre-trained model. Despite the constant publication of new annot...

With growing interest in laboratory automation and high-throughput systems, the amount of generated experimental data is rapidly increasing while analysis methods still require many manual work hours from experts. This is prevalent in X-ray photoelectron spectroscopy (XPS), where quantification is a complex, time-consuming, and error-prone task. We...

Extracting structured information from scientific works is challenging as sought parameters or properties are often scattered across lengthy texts. We introduce a novel iterative approach using Large Language Models (LLMs) to automate this process. Our method first condenses scientific literature, preserving essential information in a dense format,...

X-ray diffraction (XRD) is commonly used to analyze phase compositions of crystalline samples. Medical applications include the analysis of biotechnological materials and gall- and kidney stones, where composition can inform pathology assessment. XRD analysis methods like Rietveld refinement requires expert knowledge, and multi-phase sample analysi...

Background The growth and drug response of tumors are influenced by their stromal composition, both in vivo and 3D-cell culture models. Cell-type inherent features as well as mutual relationships between the different cell types in a tumor might affect drug susceptibility of the tumor as a whole and/or of its cell populations. However, a lack of si...

Lumbar spine problems are ubiquitous, motivating research into targeted imaging for treatment planning and guided interventions. While high resolution and high contrast CT has been the modality of choice, MRI can capture both bone and soft tissue without the ionizing radiation of CT albeit longer acquisition time. The critical trade-off between con...

Evenness is an essential indicator of road quality. Accelerometer sensors in smartphones offer an accessible and cost-efficient solution for monitoring road evenness. However, the accelerometer signal from smartphones is influenced by various internal and external factors beyond the road’s actual evenness. External factors, in particular, can intro...

The droplet microarray (DMA) platform is a powerful tool for high‐throughput biological and chemical applications, enabling miniaturization and parallelization of experimental processes. Capable of holding hundreds of nanoliter droplets, it facilitates the screening and analysis of samples, such as cells, bacteria, embryos, and spheroids. Handling...

3D cell culture models replicate tissue complexity and aim to study cellular interactions and responses in a more physiologically relevant environment compared to traditional 2D cultures. However, the spherical structure of these models makes it difficult to extract meaningful data, necessitating advanced techniques for proper analysis. In silico s...

Biomedical research increasingly relies on 3D cell culture models and AI-based analysis can potentially facilitate a detailed and accurate feature extraction on a single-cell level. However, this requires for a precise segmentation of 3D cell datasets, which in turn demands high-quality ground truth for training. Manual annotation, the gold standar...

Spheroids have become principal three-dimensional models to study cancer, developmental processes, and drug efficacy. Single-cell analysis techniques have emerged as ideal tools to gauge the complexity of cellular responses in these models. However, the single-cell quantitative assessment based on 3D-microscopic data of the subcellular distribution...

3D cell culture models replicate tissue complexity, aiming to study cellular interactions and responses in a more physiologically relevant environment compared to traditional 2D cultures. However, the spherical structure of these models makes it difficult to extract meaningful data, necessitating advanced techniques for proper analysis. In silico s...

Electron microscopy is indispensable for examining the morphology and composition of solid materials at the sub-micron scale. To study the powder samples that are widely used in materials development, scanning electron microscopes (SEMs) are increasingly used at the laboratory scale to generate large datasets with hundreds of images. Parsing these...

Hymenoptera has some of the highest diversity and number of individuals among insects. Many of these species potentially play key roles as food sources, pest controllers and pollinators. However, little is known about the diversity and biology and ~80% of the species have not yet been described. Classical taxonomy based on morphology is a rather sl...

We propose a novel image-analysis based machine-learning approach to the fully-automated identification of the optical quality, of functional properties, and of manufacturing parameters in the field of 2D inkjet-printed test structures of conductive traces. To this end, a customizable modular concept to simultaneously identify or predict dissimilar...

Spheroids have become principal three-dimensional biological models to study cancer, developmental processes, and drug efficacy. For spheroid generation, ultra-low attachment plates are noteworthy due to their simplicity, compatibility with automation, and experimental and commercial accessibility. Nonetheless, it is unknown whether and to what deg...

The objective of this paper is to study the impact of limited datasets on deep learning techniques and conventional methods in semantic image segmentation and to conduct a comparative analysis in order to determine the optimal scenario for utilizing both approaches. We introduce a synthetic data generator, which enables us to evaluate the impact of...

The nanoscale arrangement of ligands can have a major effect on the activation of membrane receptor proteins and thus cellular communication mechanisms. Here we report on the technological development and use of tailored DNA origami-based molecular rulers to fabricate “Multiscale Origami Structures As Interface for Cells” (MOSAIC), to enable the sy...

By leveraging data from both RADAR and LiDAR sensors, the accuracy of object detection and other autonomous driving tasks significantly improves in comparison to single-sensor approaches. This paper introduces a novel adaptation of the low-level fusion variant of Complex-YOLO, specifically designed to cope with sensor disturbances. We develop and i...

New materials are frequently synthesized and optimized with the explicit intention to improve their properties to meet the ever‐increasing societal requirements for high‐performance and energy‐efficient electronics, new battery concepts, better recyclability, and low‐energy manufacturing processes. This often involves exploring vast combinations of...

The workshop proceedings contain the contributions of the 33rd workshop "Computational Intelligence" which will take place from 23.11. - 24.11.2023 in Berlin. The focus is on methods, applications and tools for ° Fuzzy systems, ° Artificial Neural Networks, ° Evolutionary algorithms and ° Data mining methods as well as the comparison of methods on...

A touch-evoked response of zebrafish larvae provides information on the mechanism of the gene functional expressions. Recently, an automated system has been developed for precise and repeated touch-response experimentation with minor human intervention. To quantify the collected data, we propose a fully automated multi-larvae touch-response behavio...

Nowadays, Machine Learning (ML) is experiencing tremendous popularity that has never been seen before. The operationalization of ML models is governed by a set of concepts and methods referred to as Machine Learning Operations (MLOps). Nevertheless, researchers, as well as professionals, often focus more on the automation aspect and neglect the con...

We introduce a lightweight framework for semantic segmentation that utilizes structured classifiers as an alternative to deep learning methods. Biomedical data is known for being scarce and difficult to label. However, this framework provides a lightweight, easy-to-apply, and fast-to-train approach that can be adapted to changes in image material t...

U-Net is the go-to approach for biomedical segmentation applications. However, it is not designed to segment overlapping objects, a challenge Mask R-CNN has shown to have great potential in. Yet, Mask R-CNN receives little attention in biomedicine. Hence, we evaluate both approaches on a publicly available biomedical dataset. We find that Mask RCNN...

Supervised Neural Networks are used for segmentation in many biological and biomedical applications. To omit the time-consuming and tiring process of manual labeling, unsupervised Generative Adversarial Networks (GANs) can be used to synthesize labeled data. However, the training of GANs requires extensive computation and is often unstable. Due to...

Background: Despite promising results of targeted therapy approaches, non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related death. Tripartite motif containing 11 (TRIM11) is part of the TRIM family of proteins, playing crucial roles in tumor progression. TRIM11 serves as an oncogene in various cancer types and has been rep...

To aid the development of machine learning models for automated spectroscopic data classification, we created a universal synthetic dataset for the validation of their performance. The dataset mimics the characteristic appearance of experimental measurements from techniques such as X-ray diffraction, nuclear magnetic resonance, and Raman spectrosco...

To address the challenge of drug resistance and limited treatment options for recurrent gliomas with IDH1 mutations, a highly miniaturized screening of 2208 FDA‐approved drugs is conducted using a high‐throughput droplet microarray (DMA) platform. Two patient‐derived temozolomide‐resistant tumorspheres harboring endogenous IDH1 mutations (IDH1mut)...

The analysis of 3D microscopic cell culture images plays a vital role in the development of new therapeutics. While 3D cell cultures offer a greater similarity to the human organism than adherent cell cultures, they introduce new challenges for automatic evaluation, like increased heterogeneity. Deep learning algorithms are able to outperform conve...

Cancer is a devastating disease and the second leading cause of death worldwide. However, the development of resistance to current therapies is making cancer treatment more difficult. Combining the multi-omics data of individual tumors with information on their in-vitro Drug Sensitivity and Resistance Test (DSRT) can help to determine the appropria...

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge...

italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective : The scarcity of high-quality annotated data is omnipresent in machine learning. Especially in biomedical segmentation applications, experts need to spend a lot of their time into annotating due to the complexity. Hence, methods to reduce su...

Deep learning models for image segmentation achieve high-quality results, but need large amounts of training data. Training data is primarily annotated manually, which is time-consuming and often not feasible for large-scale 2D and 3D images. Manual annotation can be reduced using synthetic training data generated by generative adversarial networks...

Various research domains use machine learning approaches because they can solve complex tasks by learning from data. Deploying machine learning models, however, is not trivial and developers have to implement complete solutions which are often installed locally and include Graphical User Interfaces (GUIs). Distributing software to various users on-...

Various research domains use machine learning approaches because they can solve complex tasks by learning from data. Deploying machine learning models, however, is not trivial and developers have to implement complete solutions which are often installed locally and include Graphical User Interfaces (GUIs). Distributing software to various users on-...

Various research domains use machine learning approaches because they can solve complex tasks by learning from data. Deploying machine learning models, however, is not trivial and developers have to implement complete solutions which are often installed locally and include Graphical User Interfaces (GUIs). Distributing software to various users on-...

The article assesses the developments in automated phenotype pattern recognition: Potential spikes in classification performance, even when facing the common small-scale biomedical data set, and as a reader, you will find out about changes in the development effort and complexity for researchers and practitioners. After reading, you will be aware o...

Deep learning models achieve high-quality results in image processing. However, to robustly optimize parameters of deep neural networks, large annotated datasets are needed. Image annotation is often performed manually by experts without a comprehensive tool for assistance which is time- consuming, burdensome, and not intuitive. Using the here pres...

Human induced pluripotent stem cells (hiPSCs) are crucial for disease modeling, drug discovery, and personalized medicine. Animal‐derived materials hinderapplications of hiPSCs in medical fields. Thus, novel and well‐defined substrate coatings capable of maintaining hiPSC pluripotency are important for advancing biomedical applications of hiPSCs. H...

Multidrug‐resistant (MDR) bacteria is a severe threat to public health. Therefore, it is urgent to establish effective screening systems for identifying novel antibacterial compounds. In this study, a highly miniaturized droplet microarray (DMA) based high‐throughput screening system is established to screen over 2000 compounds for their antimicrob...

Several algorithms for the normalization of proteomic data are currently available, each based on a priori assumptions. Among these is the extent to which differential expression (DE) can be present in the dataset. This factor is usually unknown in explorative biomarker screens. Simultaneously, the increasing depth of proteomic analyses often requi...

To assist in the development of machine learning methods for automated classification of spectroscopic data, we have generated a universal synthetic dataset that can be used for model validation. This dataset contains artificial spectra designed to represent experimental measurements from techniques including X-ray diffraction, nuclear magnetic res...

Sensory data is essential for the training of methods in autonomous driving like object detection, odometry, or SLAM. MEMS LiDAR sensors can be very valuable for autonomous vehicles because they are less prone to shock and wear compared to motorized optomechanical LiDAR sensors. Recording real-world data is complicated and expensive. An alternative...

Sensory data is essential for the training of methods in autonomous driving like object detection, odometry, or SLAM. MEMS LiDAR sensors can be very valuable for autonomous vehicles because they are less prone to shock and wear compared to motorized optomechanical LiDAR sensors. Recording real-world data is complicated and expensive. An alternative...

Sensory data is essential for the training of methods in autonomous driving like object detection, odometry, or SLAM. MEMS LiDAR sensors can be very valuable for autonomous vehicles because they are less prone to shock and wear compared to motorized optomechanical LiDAR sensors. Recording real-world data is complicated and expensive. An alternative...

In vitro cell‐based experiments are particularly important in fundamental biological research. Microscopy‐based readouts to identify cellular changes in response to various stimuli are a popular choice, but gene expression analysis is essential to delineate the underlying molecular dynamics in cells. However, cell‐based experiments often suffer fro...

Comprehensible and high-quality automated cell nucleus segmentation and classification are required to assist pathol-ogists in their decision making. Commonly, cell nucleus segmentation and classification are either treated as separate tasks or split into different branches of a convolutional neural network. In this contribution, we present our joi...

Digitalisation trends of Industry 4.0 and Internet of Things led to an unprecedented growth of manufacturing data. This opens new horizons for data-driven methods, such as Machine Learning (ML), in monitoring of manufacturing processes. In this work, we propose ML pipelines for quality monitoring in Resistance Spot Welding. Previous approaches most...

Automated cell nucleus segmentation and classification are required to assist pathologists in their decision making. The Colon Nuclei Identification and Counting Challenge 2022 (CoNIC Challenge 2022) supports the development and comparability of segmentation and classification methods for histopathological images. In this contribution, we describe...

Deep learning increasingly accelerates biomedical research, deploying neural networks for multiple tasks, such as image classification, object detection, and semantic segmentation. However, neural networks are commonly trained supervised on large-scale, labeled datasets. These prerequisites raise issues in biomedical image recognition, as datasets...

Supervised deep learning approaches for automated diagnosis support require datasets annotated by experts. Intra-annotator variability of a single annotator and inter-annotator variability between annotators can affect the quality of the diagnosis support. As medical experts will always differ in annotation details, quantitative studies concerning...

Finding the optimal location and rotation for every sensor in a multi-sensor setup is not trivial because there are many possible configurations to compare and the space of all feasible positions for the sensors is non-convex. The configuration includes the position and rotation (yaw, pitch, and roll) of the sensors on a car, it influences the abil...

Grid structures are common in high-throughput assays to parallelize experiments in biochemical or biological experiments. Manual analysis of grid images is laborious, time-consuming, expensive, and critical in terms of reproducibility. However, it is still common to do such analysis manually, as there is no standardized software for automated analy...

Touch response of zebrafish larvae refers to the behaviors after the mechanical stimuli that varies under different conditions (different drugs), and statistical analysis of the behaviors in the organism level is becoming more attractive in drug screening. Collecting repeatable and reliable experimental data on the alive animals in a high-throughpu...

Recent research in the field of computer vision strongly focuses on deep learning architectures to tackle image processing problems. Deep neural networks are often considered in complex image processing scenarios since traditional computer vision approaches are expensive to develop or reach their limits due to complex relations. However, a common c...

The biocompatibility of medical sensors is of great importance. In order to prevent harm of the patient during measurement, this aspect must be considered throughout the entire design process. Biocompatibility can be achieved by various methods. For example, the sensor can be encapsulated, only biocompatible materials can be used for the sensor, or...

Simple and rapid imaging and analysis of 2D and 3D cell culture compatible with miniaturized arrays of nanoliter droplets are essential for high-throughput screening and personalized medicine applications. In this study, we have developed a simple one-step, cost-effective and sensitive colorimetric method for the analysis of cell viability in 2D an...

Pilot study examining a profession-oriented rehabilitation concept for nursing professions Objectives: Nursing professions are associated with high levels of psychological distress, high numbers of absent days and premature retirement. To achieve higher return-to-work rates, psychosomatic rehabilitation is expected to offer treatments tailored to w...

Zusammenfassung Durch das Aufkommen von kostengünstigen und mobilen Kameras ist es möglich, einfach und flächendeckend ungeordnete Bilddaten in großem Umfang zu erheben. Dadurch können Veränderungen von Gebieten bzw. Objekten aufgezeichnet werden. Die Abbildung von objektbezogenen Veränderungen in aufgezeichneten Bildaufnahmen ist jedoch eine Herau...

Image processing techniques are widely used within automotive series production, including production of electronic control units (ECUs). Deep learning approaches have made rapid advances during the last years, but are not prominent in those industrial settings yet. One major obstacle is the lack of suitable training data. We adapt the recently dev...

When approaching thyroid gland tumor classification, the differentiation between samples with and without “papillary thyroid carcinoma-like” nuclei is a daunting task with high inter-observer variability among pathologists. Thus, there is increasing interest in the use of machine learning approaches to provide pathologists real-time decision suppor...

Touch-response experimentation in zebrafish helps researchers better understand the link between genetics, drug effects, and behaviors. However, commonly manually conducted experimentation cannot fulfill a high-throughput screening and often delivers low accuracy and lacks reproducibility. Thus, the main aim of this work is to establish a fully aut...

Droplet wetting on a solid substrate is affected by the surface heterogeneity. Introducing patterned wettability on the solid substrate is expected to engender anisotropic wetting morphologies, thereby manipulating droplet wetting behaviors. However, when the droplet size is comparable with that of the surface heterogeneity, the wetting morphologie...

Light‐based microfabrication techniques constitute an indispensable approach to fabricate tissue assemblies, benefiting from noncontact spatially and temporarily controlled manipulation of soft matter. Light‐triggered degradation of soft materials, such as hydrogels, is important in tissue engineering, bioprinting, and related fields. The photoresp...

Ozone therapy is established in clinical applications since many decades, such as treatment of infections or herniated discs. However, in-line monitoring of ozone concentrations is challenging and therefore not performed by default. We developed an experimental setup to measure the in-line ozone concentration in water in real-time with a spectrosco...

Measuring the similarity between point clouds is required in many areas. In autonomous driving, point clouds for 3D perception are estimated from camera images but these estimations are error-prone. Furthermore, there is a lack of measures for quality quantification using ground truth. In this paper, we derive conditions point cloud comparisons nee...

Day length in conjunction with seasonal cycles affects many aspects of animal biology. We have studied photoperiod-dependent alterations of complex behaviour in the teleost, medaka (Oryzias latipes), a photoperiodic breeder, in a learning paradigm whereby fish have to activate a sensor to obtain a food reward. Medaka were tested under a long (14:10...

Background: The approval of immune checkpoint inhibitors in combination with specific diagnostic biomarkers presents new challenges to pathologists as tumor tissue needs to be tested for expression of programmed death-ligand 1 (PD-L1) for a variety of indications. As there is currently no requirement to use companion diagnostic assays for PD-L1 tes...

Within the domain of analyzing powder X-ray diffraction (XRD) scans, manual examination of the recorded data is still the most popular method, but it requires some expertise and is time consuming. The usual workflow for the phase-identification task involves software for searching databases of known compounds and matching lists of d spacings and re...

Behavioral analysis of moving animals relies on a faithful recording and track analysis to extract relevant parameters of movement. To study group behavior and social interactions, often simultaneous analyses of individuals are required. To detect social interactions, for example to identify the leader of a group as opposed to followers, one needs...

Perception systems, to a large extent, rely on neural networks. Commonly, the training of neural networks uses a finite amount of data. The usual assumption is that an appropriate training dataset is available, which covers all relevant domains. This abstract will follow the example of different lighting conditions in autonomous driving scenarios....

In material sciences, X-ray diffraction (XRD) or nuclear magnetic response (NMR) are methods to generate one-dimensional signals, describing intensities over an angle or a chemical shift. Each material has a characteristic profile and unknown samples are typically matched to known references. Automatic classification of one-dimensional signal patte...

The analysis of microscopic images from cell cultures plays an important role in the development of drugs. The segmentation of such images is a basic step to extract the viable information on which further evaluation steps are build. Classical image processing pipelines often fail under heterogeneous conditions. In the recent years deep neuronal ne...

Object detectors are central to autonomous driving and widely used in driver assistance systems. Object detectors are trained on a finite amount of data, within a specific domain. This hampers detection performance when applying object detectors to samples from other domains during inference, an effect known as domain gap. Domain gap is a concern f...