  We developed a deep learning framework that helps to automatically identify
and segment lung cancer areas in patients' tissue specimens. The study was
based on a cohort of lung cancer patients operated at the Uppsala University
Hospital. The tissues were reviewed by lung pathologists and then the cores
were compiled to tissue micro-arrays (TMAs). For experiments, hematoxylin-eosin
stained slides from 712 patients were scanned and then manually annotated. Then
these scans and annotations were used to train segmentation models of the
developed framework. The performance of the developed deep learning framework
was evaluated on fully annotated TMA cores from 178 patients reaching
pixel-wise precision of 0.80 and recall of 0.86. Finally, publicly available
Stanford TMA cores were used to demonstrate high performance of the framework
qualitatively.
