Smart Pathology

Abstract

Automated analysis of digital pathology images when used in a tele-health setting can have a transformative impact on underserved communities in the developing world. However, the vast majority of existing image analysis algorithms are trained on slide images acquired via expensive Whole-Slide Imaging (WSI) scanners. High scanner cost is a key bottleneck preventing large-scale adoption of automated digital pathology solutions in the developing countries. In this work, we investigate the possibility of performing automated image analysis and quantification using images captured from the eyepiece of a microscope via a smartphone. There are some challenges associated with using a Microscope plus smartphone setup (e.g. non-uniform intensity and illumination across different images). Will these factor have impact on the performance of the machine learning algorithms? The mitosis detection application is considered as a use case, Faster-RCNN is implemented for Mitosis detection and Segmentation. Results are compared with a mitosis dataset of comparable size to obtain an estimate of how much (if any) performance degradation results by employing microscope images.