8 papers
Data Synthesis Improves 3D Myotube Instance Segmentation
David Exler, Nils Friederich, Martin Krüger +5
Myotubes are multinucleated muscle fibers serving as key model systems for studying muscle physiology, disease mechanisms, and drug responses. Mechanistic studies and drug screenin…
Self-Supervised Learning Strategies for a Platform to Test the Toxicity of New Chemicals and Materials
Thomas Lautenschlager, Nils Friederich, Angelo Jovin Yamachui Sitcheu +4
High-throughput toxicity testing offers a fast and cost-effective way to test large amounts of compounds. A key component for such systems is the automated evaluation via machine l…
Are Foundation Models Ready for Industrial Defect Recognition? A Reality Check on Real-World Data
Simon Baeuerle, Pratik Khanna, Nils Friederich +4
Foundation Models (FMs) have shown impressive performance on various text and image processing tasks. They can generalize across domains and datasets in a zero-shot setting. This c…
EAP4EMSIG -- Enhancing Event-Driven Microscopy for Microfluidic Single-Cell Analysis
Nils Friederich, Angelo Jovin Yamachui Sitcheu, Annika Nassal +12
Microfluidic Live-Cell Imaging (MLCI) yields data on microbial cell factories. However, continuous acquisition is challenging as high-throughput experiments often lack real-time in…
LeDiFlow: Learned Distribution-guided Flow Matching to Accelerate Image Generation
Pascal Zwick, Nils Friederich, Maximilian Beichter +3
Enhancing the efficiency of high-quality image generation using Diffusion Models (DMs) is a significant challenge due to the iterative nature of the process. Flow Matching (FM) is…
Decision-Focused Fine-Tuning of Time Series Foundation Models for Dispatchable Feeder Optimization
Maximilian Beichter, Nils Friederich, Janik Pinter +7
Time series foundation models provide a universal solution for generating forecasts to support optimization problems in energy systems. Those foundation models are typically traine…