  Current pathology workflow involves staining of thin tissue slices, which
otherwise would be transparent, followed by manual investigation under the
microscope by a trained pathologist. While the hematoxylin and eosin (H&E)
stain is well-established and a cost-effective method for visualizing histology
slides, its color variability across preparations and subjectivity across
clinicians 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 of
preparation and human bias. Additionally, the sensitivity of SLIM to collagen
fibers yields information relevant to patient outcome, which is not available
in H&E. Here, we show that deep learning and SLIM can form a powerful
combination for screening applications: training on 1,660 SLIM images of colon
glands and validating on 144 glands, we obtained a benign vs. cancer
classification accuracy of 99%. We envision that the SLIM whole slide scanner
presented here paired with artificial intelligence algorithms may prove
valuable as a pre-screening method, economizing the clinician's time and
effort.
