Deep Learning for Histopathological Image Analysis

Anatomical Pathology dates back to the 19th century when Rudolf Virchow introduced his concept of cellular pathology and when the technical improvements of light microscopy enabled widespread use of structural criteria to define diseases. Since then, the quality of optical instruments has been constantly evolving. However the central element of the diagnostic process remains the knowledge and experience of pathologists visually classifying observations according to internationally agreed guidelines (e.g., World Health Organisation (WHO) classification), and much of the pre-analytical steps of specimen preparation (e.g., fixation, embedding, sectioning, staining) is only partially automated and still requires many manual steps. Thanks to the recent advent and costeffectiveness of digital scanners, tissue histopathology slides can now be fully digitized and stored as Whole Slide Images (WSI). With the availability and analysis of a much larger set of variables combined with sophisticated imaging and analytic techniques, the traditional paradigm of pathology based on visually descriptive microscopy can be complemented and substantially improved by digital pathology, utilizing screen-based visualization of digital tissue sections and novel analysis tools potentially combining the conventional evaluation by pathologists with a computerbased diagnostic aid system. A central element of such evolving medical utilities and decision support systems will be image analysis, a field in which Deep Learning (DL) has recently made immense progress, notably the work of Lecun et al. [33] on Convolutional Neural Networks (CNNs) and especially the development of very large Artificial Neural Networks (ANNs) that are revolutionizing the field. Indeed, they have surpassed all existing image processing methods in most fields (segmentation, object detection, classification, etc.). All current methods applied to histopathological image analysis will be presented as well as the future technological issues and challenges of this discipline