Our motivation for addressing the variability problem in image-based measurements comes from increasing reports of irreproducibility in the Artificial Intelligence/Machine learning (AI/ML) field. Most recently, two new analyses [1, 2] put the spotlight on machine learning in health research, where lack of reproducibility and poor quality could risk harm to patients and/or lower the quality of care a patient receives. There is a need to quantify and minimize measurement uncertainties from various computational sources while leveraging all cutting-edge AI/ML approaches to image-based drug discoveries.
 M. B. A. McDermott, S. Wang, N. Marinsek, R. Ranganath, L. Foschini, and M. Ghassemi, “Reproducibility in machine learning for health research: Still a ways to go,” Sci. Transl. Med., vol. 13, no. 586, Mar. 2021, DOI: 10.1126/scitranslmed.abb1655.
 M. Roberts et al., “Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans,” Nat. Mach. Intell., vol. 3, no. 3, pp. 199--217, Mar. 2021, DOI: 10.1038/s42256-021-00307-0.
The goal of our effort is to improve reproducibility of image-based measurements and quantify the measurement uncertainty while leveraging all cutting-edge AI/ML approaches to image-based drug discoveries. In our work, we selected measurements derived from fluorescently-labeled nucleus images over samples with a variety of drug treatments and imaging variations. Such image-based measurements require
The challenges of delivering reproducible nucleus measurements lie in a lack of
In this study, we focus on the variability of image-based measurements that come from
We approach the measurement variability by
WIPP plugin registry: