1. HistomicsML2.0: Fast interactive machine learning for whole slide imaging data
- Author
-
Lee, Sanghoon, Amgad, Mohamed, Chittajallu, Deepak R., McCormick, Matt, Pollack, Brian P, Elfandy, Habiba, Hussein, Hagar, Gutman, David A, and Cooper, Lee AD
- Subjects
Quantitative Biology - Quantitative Methods ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Image and Video Processing - Abstract
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.
- Published
- 2020