  Deploying deep learning-based imaging tools across various clinical sites
poses significant challenges due to inherent domain shifts and regulatory
hurdles associated with site-specific fine-tuning. For histopathology, stain
normalization techniques can mitigate discrepancies, but they often fall short
of eliminating inter-site variations. Therefore, we present Data Alchemy, an
explainable stain normalization method combined with test time data calibration
via a template learning framework to overcome barriers in cross-site analysis.
Data Alchemy handles shifts inherent to multi-site data and minimizes them
without needing to change the weights of the normalization or classifier
networks. Our approach extends to unseen sites in various clinical settings
where data domain discrepancies are unknown. Extensive experiments highlight
the efficacy of our framework in tumor classification in hematoxylin and
eosin-stained patches. Our explainable normalization method boosts
classification tasks' area under the precision-recall curve(AUPR) by 0.165,
0.545 to 0.710. Additionally, Data Alchemy further reduces the multisite
classification domain gap, by improving the 0.710 AUPR an additional 0.142,
elevating classification performance further to 0.852, from 0.545. Our Data
Alchemy framework can popularize precision medicine with minimal operational
overhead by allowing for the seamless integration of pre-trained deep
learning-based clinical tools across multiple sites.
