Thomas Wollmann

• Dr. rer. nat.
• CTO at Merantix Momentum

Reduced heart rate variability (HRV) is an indicator of a malfunctioning autonomic nervous system. Resonant frequency breathing is a potential non-invasive means of intervention for improving the balance of the autonomic nervous system and increasing HRV. However, such breathing exercises are regarded as boring and monotonous tasks. The use of gami...

E-Learning ist schon länger als Lernmethode für Faktenwissen etabliert. Der Erfolg hängt jedoch von der Wiederholung und der Motivation des Nutzers ab. Gamifiziertes E-Learning hat den Anspruch genau diese Lücke zwischen Motivation und E-Learning zu schließen und die Lernleistung in Bezug auf Geschwindigkeit und Dauerhaftigkeit des Wissens zu erhöh...

In large scale biological experiments, like high-throughput or high-content cellular screening, the amount and the complexity of images to be analyzed is steadily increasing. To handle and process these images, well defined image processing and analysis steps need to be performed by applying dedicated workflows. Multiple software tools have emerged...

We present a human-in-the-loop system for efficient rodent behavior analysis in drug development. Addressing the time-consuming and labor-intensive nature of manual behavior categorization, this UX-optimized platform integrates AI for complex behavior prediction and active learning to identify rare events. The proposed solution leverages a cloud-na...

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analys...

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analys...

The current risk stratification in prostate cancer (PCa) is frequently insufficient to adequately predict disease development and outcome. One hallmark of cancer is telomere maintenance. For telomere maintenance, PCa cells exclusively employ telomerase, making it essential for this cancer entity. However, TERT, the catalytic protein component of th...

Automatic tracking of viral structures displayed as small spots in fluorescence microscopy images is an important task to determine quantitative information about cellular processes. We introduce a novel probabilistic approach for tracking multiple particles based on multi-sensor data fusion and Bayesian smoothing methods. The approach exploits mul...

Image segmentation is a common and challenging task in autonomous driving. Availability of sufficient pixel-level annotations for the training data is a hurdle. Active learning helps learning from small amounts of data by suggesting the most promising samples for labeling. In this work, we propose a new pool-based method for active learning, which...

Despite much recent work, detecting out-of-distribution (OOD) inputs and adversarial attacks (AA) for computer vision models remains a challenge. In this work, we introduce a novel technique, DAAIN, to detect OOD inputs and AA for image segmentation in a unified setting. Our approach monitors the inner workings of a neural network and learns a dens...

Developing, scaling, and deploying modern Machine Learning solutions remains challenging for small- and middle-sized enterprises (SMEs). This is due to a high entry barrier of building and maintaining a dedicated IT team as well as the difficulties of real-world data (RWD) compared to standard benchmark data. To address this challenge, we discuss t...

Detection of cells and particles in microscopy images is a common and challenging task. In recent years, detection approaches in computer vision achieved remarkable improvements by leveraging deep learning. Microscopy images pose challenges like small and clustered objects, low signal to noise, and complex shape and appearance, for which current ap...

High-content microscopy led to many advances in biology and medicine. This fast emerging technology is transforming cell biology into a big data driven science. Computer vision methods are used to automate the analysis of microscopy image data. In recent years, deep learning became popular and had major success in computer vision. Most of the avail...

Background Mass spectrometry imaging is increasingly used in biological and translational research because it has the ability to determine the spatial distribution of hundreds of analytes in a sample. Being at the interface of proteomics/metabolomics and imaging, the acquired datasets are large and complex and often analyzed with proprietary softwa...

Purpose Automated analysis of microscopy image data typically requires complex pipelines that involve multiple methods for different image analysis tasks. To achieve best results of the analysis pipelines, method-dependent hyperparameters need to be optimized. However, complex pipelines often suffer from the fact that calculation of the gradient of...

Background: Mass spectrometry imaging is increasingly used in biological and translational research as it has the ability to determine the spatial distribution of hundreds of analytes in a sample. Being at the interface of proteomics/metabolomics and imaging, the acquired data sets are large and complex and often analyzed with proprietary software...

Cell segmentation in microscopy images is a common and challenging task. In recent years, deep neural networks achieved remarkable improvements in the field of computer vision. The dominant paradigm in segmentation is using convolutional neural networks, less common are recurrent neural networks. In this work, we propose a new deep learning method...

Tracking of particles in fluorescence microscopy image sequences is essential for studying the dynamics of subcellular structures and virus structures. We introduce a novel particle tracking approach using an LSTM-based neural network. Our approach determines assignment probabilities jointly across multiple detections by exploiting both short and l...

Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is highly subjective and labor-intensive task. Previous efforts to automate tumor proliferation assessment by image analysis only focused on mitosis detection in predefined tumor regions. Howev...

Segmentation of cell nuclei is crucial for analyzing microscopy image data and understanding cellular processes. We performed a comparison of different approaches for segmentation of cell nuclei in difficult tissue microscopy image data of glioblastoma cells. We investigated nine different methods comprising thresholding, deformable models, region...

The progression of breast cancer can be quantified in whole-slide images of lymph nodes. We describe a novel deep learning method for classification of whole-slide images and patient level breast cancer grading. Our method is based on domain adaptation using a Cycle-Consistent Generative Adversarial Network (CycleGAN), in conjunction with a densely...

We present Bioconda (https://bioconda.github.io), a distribution of bioinformatics software for the lightweight, multi-platform and language-agnostic package manager Conda. Currently, Bioconda offers a collection of over 3000 software packages, which is continuously maintained, updated, and extended by a growing global community of more than 200 co...

The progression of breast cancer can be quantified in lymph node whole-slide images (WSIs). We describe a novel method for effectively performing classification of whole-slide images and patient level breast cancer grading. Our method utilises a deep neural network. The method performs classification on small patches and uses model averaging for bo...

Grading of tissue based on microscopic images is a common and challenging task. We propose a new method for grading of wholeslide histology images of invasive breast carcinoma, which is based on mitotic cell detection. The method combines a threshold-based attention mechanism and a deep neural network for mitotic cell detection and grading. Our mit...

Background: Reduced heart rate variability is an indicator of a malfunctioning autonomic nervous system and can be the cause or the consequence of a disease. Resonance frequency breathing is a non-invasive treatment. The breathing exercise is stated as boring. Turning a task into a video game is a current trend to increase the enjoyment. However, t...

Exergaming for adolescents is a promising field of design. Going beyond console-based exergames, smartwatches can utilize the pervasive nature afforded by modern ubiquitous devices and use physical body movement and changes of location as the basis for gameplay. In a formative study of PokéWatch, a mobile location-aware exergame based on the game P...