HistomicsML2.0: Fast interactive machine learning for whole slide imaging data
Sanghoon Lee, Mohamed T. Amgad, Deepak R. Chittajallu, Matt McCormick, Brian P. Pollack, Habiba Elfandy, Hagar M. Hussein, David A. Gutman, Lee Cooper · arXiv (Cornell University) · 2020
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.