A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images
This work addresses early diagnosis of oral cancer, which is crucial for survival, but it appears incremental as it builds on existing deep learning techniques with a novel focus selection step.
The authors tackled oral cancer screening by developing an automated pipeline for detecting cancer in whole slide cytology images, achieving improved accuracy and feasibility over previous methods.
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.