Franziska Scheurer

Machine Learning for Passive Acoustic Wildlife Monitoring: Methods for Semi-Automated Population and Species Assessment

Passive acoustic monitoring (PAM) has become a powerful tool for studying wildlife by continuously recording environmental soundscapes. However, analysing large acoustic datasets remains highly time-consuming, as recordings are often annotated manually by domain experts. In this work, we investigate how machine learning can support scalable biodiversity monitoring by enabling efficient Read more

By Franziska Scheurer, ago

Grounded Label Space Engineering for Knowledge-Centric Annotation Workflows

Building reliable AI models depends not only on how much data is annotated, but on the quality and meaning of the labels used during annotation. In many workflows, labels are flat, task-specific class names. They are easy to apply, but lack explicit semantic structure, provenance, and links to shared domain Read more

By Franziska Scheurer, ago

IQUANA: Efficient Image Annotation and Quantification

Image annotation remains a significant bottleneck in image analysis pipelines. In research settings especially, annotating large image corpora demands substantial effort, often forcing teams to compromise on quality by resorting to coarser methods like point annotations rather than full outlines. To address this, we collaborated with the Helmholtz Institute for Read more

By Franziska Scheurer, ago

Explainable Biomedical Claim Verification (Accenture) 

In the Autoprompt project funded by a grant from Accenture, one of the world’s leading consulting, technology and outsourcing companies, we focus on developing automated biomedical claim verification systems designed to assist clinicians and researchers in addressing the risks posed by misinformation in the healthcare domain.  By providing accurate, evidence-based Read more

By Franziska Scheurer, ago

Investigating Natural Language Inference Capabilities of Large Language Modes in Biomedical Claim Verification 

Left: Examples from HealthVer [1]; Right: Example of a claim that is supported and refuted by different evidence [2]  With the rapid growth of biomedical research and the concurrent rise in misinformation, ensuring the accuracy of claims about treatment effectiveness is increasingly critical. Inaccurate or misleading information can have profound Read more

By Franziska Scheurer, ago

Optimizing Relation Extraction in Medical Texts through Active Learning: A Comparative Analysis of Trade-offs

Example from n2c2 of relation extraction [1]  This work explores the effectiveness of employing Clinical BERT for Relation Extraction (RE) tasks in medical texts within an Active Learning (AL) framework. Our main objective is to optimize RE in medical texts through AL while examining the trade-offs between performance and computation Read more

By Franziska Scheurer, ago

Building A German Clinical Named Entity Recognition System without In-domain Training Data 

Clinical Named Entity Recognition (NER) is essential for extracting important medical insights from clinical narratives. Given the challenges in obtaining expert training datasets for real-world clinical applications related to data protection regulations and the lack of standardised entity types, this work represents a collaborative initiative aimed at building a German Read more

By Franziska Scheurer, ago