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Current pathology workflow involves staining of thin tissue slices, whichotherwise would be transparent, followed by manual investigation under themicroscope by a trained pathologist. While the hematoxylin and eosin (H&E)stain is well-established and a cost-effective method for visualizing histologyslides, its color variability across preparations and subjectivity acrossclinicians remain unaddressed challenges. To mitigate these challenges,recently we have demonstrated that spatial light interference microscopy (SLIM)can provide a path to intrinsic, objective markers, that are independent ofpreparation and human bias. Additionally, the sensitivity of SLIM to collagenfibers yields information relevant to patient outcome, which is not availablein H&E. Here, we show that deep learning and SLIM can form a powerfulcombination for screening applications: training on 1,660 SLIM images of colonglands and validating on 144 glands, we obtained a benign vs. cancerclassification accuracy of 99%. We envision that the SLIM whole slide scannerpresented here paired with artificial intelligence algorithms may provevaluable as a pre-screening method, economizing the clinician's time andeffort.

Label-free colorectal cancer screening using deep learning and spatial light interference microscopy (SLIM)