MACHINE LEARNING IN PATHOLOGY: A PRIMER ON TECHNIQUES AND APPLICATIONS. - Levine, Adrian B.; Grewal, Jasleen K.; Jones, Steven J. M.; Yip, Stephen (Canadian Journal of Pathology, 2018)
Machine learning based algorithms have had numerous recent successes in medical image analysis. In particular, convolutional neural networks have, in the past several years, become the preferred approach for these applications, owing to their ability to extract high-level features and relationships from complex images. In this review, we discuss these computational techniques and their applications in pathology and other areas of medicine. In histopathology, machine learning has been applied to screen slides for tumour deposits, quantify morphological features and immunohistochemical staining, and classify disease types. This can be used both as a quality assurance mechanism, as well as to extract information from slides that would not discernible by visual inspection. With state-of-the-art computer algorithms available as open source and the increasing effectiveness of slide digitization technology, we anticipate rapid advances in this field in the future.