Sample-size planning tools for multivariate data

Sample size planning (SSP) is crucial for experimental planning but is not well-established for spectroscopic and image data, especially in combination with deep learning. The existing approaches are typically quite complex for routine use in experimental planning. To make the existing approaches more accessible, we developed web-based tools for the existing approaches. Besides, we extended the approach to imaging data and deep learning by introducing transfer learning in the SSP pipeline. ACKNOWLEDGMENT: Financial support from the EU, the TMWWDG, the TAB, the BMBF, the DFG, the Carl-Zeiss Foundation, and the Leibniz Association is greatly acknowledged. This work is supported by the BMBF, funding program Photonics Research Germany (LPI-BT3-IPHT, FKZ: 13N15708) and is integrated into the Leibniz Center for Photonics in Infection Research (LPI). The LPI initiated by Leibniz-IPHT, Leibniz-HKI, Friedrich Schiller University Jena, and Jena University Hospital is part of the BMBF national roadmap for research infrastructures.