  Extracting quantitative phenotypic information from whole-slide images
presents significant challenges for investigators who are not experienced in
developing image analysis algorithms. We present new software that enables
rapid learn-by-example training of machine learning classifiers for detection
of histologic patterns in whole-slide imaging datasets. HistomicsML2.0 uses
convolutional networks to be readily adaptable to a variety of applications,
provides a web-based user interface, and is available as a software container
to simplify deployment.
