Automatic Colorectal Cancer Screening Using Deep Learning in Spatial Light Interference Microscopy Data

Jingfang K. Zhang, Michael Fanous, Nahil Sobh, Andre Kajdacsy-Balla, Gabriel Popescu

Research output: Contribution to journalArticlepeer-review


The surgical pathology workflow currently adopted by clinics uses staining to reveal tissue architecture within thin sections. A trained pathologist then conducts a visual examination of these slices and, since the investigation is based on an empirical assessment, a certain amount of subjectivity is unavoidable. Furthermore, the reliance on external contrast agents such as hematoxylin and eosin (H&E), albeit being well-established methods, makes it difficult to standardize color balance, staining strength, and imaging conditions, hindering automated computational analysis. In response to these challenges, we applied spatial light interference microscopy (SLIM), a label-free method that generates contrast based on intrinsic tissue refractive index signatures. Thus, we reduce human bias and make imaging data comparable across instruments and clinics. We applied a mask R-CNN deep learning algorithm to the SLIM data to achieve an automated colorectal cancer screening procedure, i.e., classifying normal vs. cancerous specimens. Our results, obtained on a tissue microarray consisting of specimens from 132 patients, resulted in 91% accuracy for gland detection, 99.71% accuracy in gland-level classification, and 97% accuracy in core-level classification. A SLIM tissue scanner accompanied by an application-specific deep learning algorithm may become a valuable clinical tool, enabling faster and more accurate assessments by pathologists.
Original languageEnglish (US)
Article number716
Issue number4
StatePublished - Feb 17 2022


  • spatial light interference microscopy
  • label-free
  • mask R-CNN
  • deep learning
  • automated colorectal cancer screening
  • Deep learning
  • Spatial light interference microscopy
  • Automated colorectal cancer screening
  • Label-free
  • Mask R-CNN

ASJC Scopus subject areas

  • Medicine(all)


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