Advanced deep learning applications in diagnostic pathology

Abstract

For many years, pathologists have performed histopathological diagnoses by observing tissue specimens under a microscope. Recently, however, it has become possible to scan whole slides of tissue specimens using a dedicated slide scanner and store the resultant high-resolution digital images, i.e., whole slide images. This has led to the emergence of digital pathology, a field in which whole slide images are used for histopathological diagnoses. This field is gradually expanding, especially in large hospitals such as university hospitals. In addition, dramatic advancements in image recognition technology have been made since 2012 when deep learning won the general image recognition competition ILSVRC with overwhelming accuracy. Subsequently, deep learning has been applied to various medical images, including X-ray, ocular fundus, and skin images, and is reported to have achieved generalist- or even professional-level diagnostic accuracy in each field. Similarly, the use of deep learning, directed towards digital histopathological images, for assistance with pathological diagnoses is gradually becoming practicable, especially for diseases in which many cases occur. Recently, advanced applications have been developed such as searching for similar cases, predicting genetic mutations from histological images, and generating special stained images from hematoxylin and eosin-stained images. These emerging applications have the potential to greatly expand the field of diagnostic pathology and contribute to the further development of medicine. In this review, we introduce the use of deep learning technology in the field, detail the current advanced applications, and speculate on future perspectives.