Assessment and Elimination of Inflammatory Cell: A Machine Learning Approach in Digital Cytology

In automatic cytology image diagnosis, the false-positive or false-negative often come up with inflammatory cells that obscure the identification of abnormal or normal cells. These phenotypes are presented in the similar appearance in shape, color and texture with cells to detect. In this paper, to evaluate the inflammation and eliminate their disturbances of recognizing cells of interests, we propose a two-stage framework containing a deep learning based neural network to detect and estimate the proportions of inflammatory cells, and a morphology based image processing architecture to eliminate them from the digital images with image inpainting. For performance evaluation, we apply the framework to our collected real-life clinical cytology slides presented with a variety of complexities. We evaluate the tests on sub-images cropped from 49 positive and 49 negative slides from different patients, each at the magnification rate of 40×. The experiments shows an accurate profile of the coverage of inflammation in the whole slide images, as well as their proportion in all the cells presented in the image. Confirmed by cytotechnologists, more than 96.0% of inflammatory cells are successfully detected at pixel level and well-inpainted in the cytology images without bringing new recognition problem.