A Digital Pathology Application for Whole-Slide Histopathology Image Analysis based on Genetic Algorithm and Convolutional Networks

The last decade Digital Pathology is coming as a relevant and promising area for cancer research and clinical practice thanks to two main trends, 1) the availability of whole slide scanners for complete pathology slide digitalization, and 2) the development of several computational method for histopathology image analysis. However, there are very few works addressed to analyze the whole-slide digitized images (WSI) because their large resolution (e.g. 80,000 x 80,000 pixels at 40X magnification) resulting in huge computational cost for automatic analysis. This paper presents an application design of a meta-heuristic optimization method based on a genetic algorithm (GA) for exploration and exploitation of regions of interest for diagnosis in a WSI in combination with a Convolutional Neural Network (CNN) trained in previous works [10], [11]. The preliminary results show that presented solution scales in computing time given the initial number of samples (initial population). The developed application in Java including the GA method for WSI analysis could be used for diagnosis support by pathologists thanks of its usability and visual interpretability through a probability map of the invasive tumor regions in the WSI.