Author

Daniel Grillmeyer

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#artificial intelligence Preprint Aug 2026

MedSegBenchmarker: A Raw-Count-First Framework for Controlled 2D Medical Image Segmentation Benchmarks

MEDSEGBENCHMARKER (MSB), a configuration-driven framework for controlled benchmarking of 2D MIS, provides a practical and extensible basis for making benchmark conditions and evaluation choices explicit and reproducible.

Vanessa Borst, Lukas Horn, Daniel Grillmeyer et al. · 1 citation

Review Jul 2026

Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study

Cluster-based Sequential Feature Selection (CSFS), a novel, model-agnostic, clustering-based wrapper method for automatic, efficient, and reliable feature selection in renewable energy prediction pipelines, is proposed and empirically evaluated on both use cases.

Daniel Grillmeyer, Marius Hadry, Michael Stenger et al. · arXiv.org · 0 citations