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

Sanghoon Lee; Mohamed Amgad; Deepak R. Chittajallu; Matt McCormick; Brian P Pollack; Habiba Elfandy; Hagar Hussein; David A Gutman; Lee AD Cooper

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

Quantitative Methods 2020-02-03 v1 Computer Vision and Pattern Recognition Machine Learning Image and Video Processing

Authors: Sanghoon Lee , Mohamed Amgad , Deepak R. Chittajallu , Matt McCormick , Brian P Pollack , Habiba Elfandy , Hagar Hussein , David A Gutman , Lee AD Cooper

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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.

Keywords

computational biology software and tools biomedical image analysis machine learning in genomics

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@article{arxiv.2001.11547,
  title  = {HistomicsML2.0: Fast interactive machine learning for whole slide imaging data},
  author = {Sanghoon Lee and Mohamed Amgad and Deepak R. Chittajallu and Matt McCormick and Brian P Pollack and Habiba Elfandy and Hagar Hussein and David A Gutman and Lee AD Cooper},
  journal= {arXiv preprint arXiv:2001.11547},
  year   = {2020}
}

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

Abstract

Keywords

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