Björn-Ivo Bachmann

Abstract The establishment of robust machine learning workflows for microstructure analysis requires datasets that systematically capture the variance introduced by the imaging pipeline. While existing studies in this field predominantly rely on well-curated, homogeneous datasets optimized for high classification performance, these conditions rarely reflect the variance encountered in real-world settings.

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