Publikationen

Automated tumor segmentation of histologic images is crucial for the development of computer-assisted diagnostic workflows aiming at accurate prognostication. We present a dataset of coarse annotations of over 1,000 highresolution breast tumor images from The cancer genome atlas breast invasive carcinoma (TCGA-BRCA) repository, each annotated with binary segmentation masks that delineate coarse tumor areas. Additionally, a subset of 20 images includes fine-grained annotations, providing precise delineation of tumor boundaries beyond the broader outlines used in coarse annotations. Initial evaluations using U-Net and DeepLabv3 models show promising segmentation performance. On a held-out, coarsely annotated test set, U-Net achieved an average intersection over union (IoU) score of 0.795, while DeepLabv3 scored 0.783. On the finely annotated subset of this test set, U-Net reached an average IoU of 0.746, with DeepLabv3 slightly outperforming at 0.765. The public availability of this dataset aims to support research in automated tumor analysis, advancing diagnostic workflows and thereby ultimately improving breast cancer management.

"Assessment of the density of mitotic figures (MFs) in histologic tumor sections is an important prognostic marker for many tumor types, including breast cancer. Recently, it has been reported in multiple works that the quantity of MFs with an atypical morphology (atypical MFs, AMFs) might be an independent prognostic criterion for breast cancer. AMFs are an indicator of mutations in the genes regulating the cell cycle and can lead to aberrant chromosome constitution (aneuploidy) of the tumor cells. To facilitate further research on this topic using pattern recognition, we present the first ever publicly available dataset of atypical and normal MFs (AMi-Br). For this, we utilized two of the most popular MF datasets (MIDOG 2021 and TUPAC) and subclassified all MFs using a three expert majority vote. Our final dataset consists of 3,720 MFs, split into 832 AMFs (22.4\%) and 2,888 normal MFs (77.6\%) across all 223 tumor cases in the combined set. We provide baseline classification experiments to investigate the consistency of the dataset, using a Monte Carlo cross-validation and different strategies to combat class imbalance.We found an averaged balanced accuracy of up to 0.806 when using a patch-level data set split, and up to 0.713 when using a patient-level split."

This research project investigates how adaptive Augmented Reality (AR) applications can improve the efficiency of drone operations and the experience of operators. Drones, particularly Unmanned Aerial Vehicles (UAVs), are increasingly employed in search and rescue (SAR) missions due to their rapid deployment capabilities and the ability to access hazardous areas or viewpoints that are otherwise unreachable. Operating drones in these high-demanding scenarios requires operators to possess certain cognitive abilities to manage the UAV effectively. Our research aims to develop a situation-aware interaction model that considers data from the task, the UAVs, and the human operator, offering support during demanding missions or low abilities. This model will be integrated into an AR application designed to adjust to the current situation, thereby enhancing operational outcomes and user experience.

This research project investigates how adaptive Augmented Reality (AR) applications can improve the efficiency of drone operations and the experience of operators. Drones, particularly Unmanned Aerial Vehicles (UAVs), are increasingly employed in search and rescue (SAR) missions due to their rapid deployment capabilities and the ability to access hazardous areas or viewpoints that are otherwise unreachable. Operating drones in these high-demanding scenarios requires operators to possess certain cognitive abilities to manage the UAV effectively. Our research aims to develop a situation-aware interaction model that considers data from the task, the UAVs, and the human operator, offering support during demanding missions or low abilities. This model will be integrated into an AR application designed to adjust to the current situation, thereby enhancing operational outcomes and user experience.

This project aims to explore how data-driven methods can be used to enhance humans’ problem-solving skills in spatial tasks and how they can be integrated into immersive tutoring systems. The goal is to provide an enhanced, state-dependent approach that is also applicable outside the laboratory, within real-life scenarios. Digital tutoring and learning systems are typically expert systems and follow a predefined training routine. Ongoing research concentrates on adaptive methods within affective- and cognition-aware systems, yet there is comparatively less investigation into recognizing and adapting problem-solving strategies. Spatial skill have been shown to be especially important for STEM (Science, Technology, Engineering, Mathematics) disciplines in general. Due to their design, immersive systems are particularly suitable for training of spatial skills and are thus frequently utilized for this.

This project aims to explore how data-driven methods can be used to enhance humans’ problem-solving skills in spatial tasks and how they can be integrated into immersive tutoring systems. The goal is to provide an enhanced, state-dependent approach that is also applicable outside the laboratory, within real-life scenarios. Digital tutoring and learning systems are typically expert systems and follow a predefined training routine. Ongoing research concentrates on adaptive methods within affective- and cognition-aware systems, yet there is comparatively less investigation into recognizing and adapting problem-solving strategies. Spatial skill have been shown to be especially important for STEM (Science, Technology, Engineering, Mathematics) disciplines in general. Due to their design, immersive systems are particularly suitable for training of spatial skills and are thus frequently utilized for this.

Studying physical rotation (i.e., rotation tasks during which figures can be physically rotated, such as through gestures) can offer insights also into problem solving processes at work during mental rotation. We present a novel method for behavioral pattern analysis which we applied to data from 2,999 physical rotation tasks gathered in-class from 50 secondary school students. The method uses normalized, resampled, time-dependent data on angular offsets between figures over time and agglomerative, correlation-based clustering. Each cluster represents a distinct behavioral pattern and its respective prototype a problem solving strategy. Results indicate that multiple strategies were employed: The dominant strategy matches the classical model of mental rotation, in which angular offsets between figures are decreased over time. For the secondary strategy, angular offsets were actually increased. A subsequent analysis shows that the secondary strategy was more frequently used for symmetric figures, possibly indicating problems with correctly matching segments across figures.

Studying physical rotation (i.e., rotation tasks during which figures can be physically rotated, such as through gestures) can offer insights also into problem solving processes at work during mental rotation. We present a novel method for behavioral pattern analysis which we applied to data from 2,999 physical rotation tasks gathered in-class from 50 secondary school students. The method uses normalized, resampled, time-dependent data on angular offsets between figures over time and agglomerative, correlation-based clustering. Each cluster represents a distinct behavioral pattern and its respective prototype a problem solving strategy. Results indicate that multiple strategies were employed: The dominant strategy matches the classical model of mental rotation, in which angular offsets between figures are decreased over time. For the secondary strategy, angular offsets were actually increased. A subsequent analysis shows that the secondary strategy was more frequently used for symmetric figures, possibly indicating problems with correctly matching segments across figures.