HistomicsML2.0: Fast interactive machine learning for whole slide imaging data

• URL: http://arxiv.org/abs/2001.11547v1
• Date: Thu, 30 Jan 2020 20:10:26 GMT
• Title: HistomicsML2.0: Fast interactive machine learning for whole slide imaging data
• Authors: Sanghoon Lee, Mohamed Amgad, Deepak R. Chittajallu, Matt McCormick, Brian P Pollack, Habiba Elfandy, Hagar Hussein, David A Gutman, Lee AD Cooper
• Abstract summary: HistomicsML2.0 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.
• Score: 6.396738205632676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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