Efficient Methods and Parallel Execution for Algorithm Sensitivity Analysis with Parameter Tuning on Microscopy Imaging Datasets

Abstract

Background: We describe an informatics framework for researchers and clinical investigators to efficiently perform parameter sensitivity analysis and autotuning for algorithms that segment and classify image features in a large dataset of high-resolution images. The computational cost of the sensitivity analysis process can be very high, because the process requires processing the input dataset several times to systemtically evaluate how output varies when input