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Label-free screening of myelin distribution in brain tissue using phase imaging with computational specificity (PICS)

In this work, we show that deep learning and SLIM can be combined to quickly deliver superior results in tissue screening applications. This concept of combining QPI label-free data with AI with the purpose of extracting molecular specificity has been recently introduced by our laboratory as phase imaging with computational specificity (PICS) [Nat. Comm., in press]. Training on ten thousand SLIM images of piglet brain tissue with the 71-layer transfer learning model Xception, we created a two-parameter classification to differentiate the gestational size: either appropriate for gestational age (AGA) or small for gestational age (SGA), and diet: either an experimental regimen high in hydrolyzed fats or a control diet, with an accuracy of 80% and 81%, respectively, and a four-parameter classification (diet and size) with 62% accuracy. These results are significant, as it would otherwise be impossible for a trained histopathologist to distinguish such discrepancies.