  Oral cancer incidence is rapidly increasing worldwide. The most important
determinant factor in cancer survival is early diagnosis. To facilitate large
scale screening, we propose a fully automated pipeline for oral cancer
detection on whole slide cytology images. The pipeline consists of fully
convolutional regression-based nucleus detection, followed by per-cell focus
selection, and CNN based classification. Our novel focus selection step
provides fast per-cell focus decisions at human-level accuracy. We demonstrate
that the pipeline provides efficient cancer classification of whole slide
cytology images, improving over previous results both in terms of accuracy and
feasibility. The complete source code is available at
https://github.com/MIDA-group/OralScreen.
