6 citations · 6 across the 10 of their papers we have counts for
13 papers
IFC-SSL-Bench: Benchmarking Self-Supervised Learning for Annotation-Efficient Multispectral Imaging Flow Cytometry
Angelo Jovin Yamachui Sitcheu, Roxana-Teodora Fetescu, Thomas Lautenschlager +6
Imaging Flow Cytometry (IFC) generates large multispectral datasets, yet the need for expert annotations bottlenecks the development of supervised deep learning models. While Self-…
napari-TurboBox: Interactive Multi-Viewer Bounding Box Annotation for AI-Guided Volumetric Bioimaging
Nils Friederich, Philip Kohle, Maximilian Beichter +11
Promptable segmentation models turn one 3D bounding box into an instance mask. Box placement therefore limits how fast microscopy volumes with thousands of objects are annotated. E…
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…
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…
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…
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…
EAP4EMSIG -- Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cells Analysis
Nils Friederich, Angelo Jovin Yamachui Sitcheu, Annika Nassal +12
Microfluidic Live-Cell Imaging (MLCI) generates high-quality data that allows biotechnologists to study cellular growth dynamics in detail. However, obtaining these continuous data…
Tracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics
J. Seiffarth, L. Blöbaum, R. D. Paul +6
Tracking the development of living cells in live-cell time-lapses reveals crucial insights into single-cell behavior and presents tremendous potential for biomedical and biotechnol…
Security Fence Inspection at Airports Using Object Detection
Nils Friederich, Andreas Specker, Jürgen Beyerer
To ensure the security of airports, it is essential to protect the airside from unauthorized access. For this purpose, security fences are commonly used, but they require regular i…
AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy
Nils Friederich, Angelo Yamachui Sitcheu, Oliver Neumann +4
In the biomedical environment, experiments assessing dynamic processes are primarily performed by a human acquisition supervisor. Contemporary implementations of such experiments f…
MLOps for Scarce Image Data: A Use Case in Microscopic Image Analysis
Angelo Yamachui Sitcheu, Nils Friederich, Simon Baeuerle +3
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 meth…