Interactive phenotyping of large-scale histology imaging data with HistomicsML.

Abstract:

:Whole-slide imaging of histologic sections captures tissue microenvironments and cytologic details in expansive high-resolution images. These images can be mined to extract quantitative features that describe tissues, yielding measurements for hundreds of millions of histologic objects. A central challenge in utilizing this data is enabling investigators to train and evaluate classification rules for identifying objects related to processes like angiogenesis or immune response. In this paper we describe HistomicsML, an interactive machine-learning system for digital pathology imaging datasets. This framework uses active learning to direct user feedback, making classifier training efficient and scalable in datasets containing 108+ histologic objects. We demonstrate how this system can be used to phenotype microvascular structures in gliomas to predict survival, and to explore the molecular pathways associated with these phenotypes. Our approach enables researchers to unlock phenotypic information from digital pathology datasets to investigate prognostic image biomarkers and genotype-phenotype associations.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Nalisnik M,Amgad M,Lee S,Halani SH,Velazquez Vega JE,Brat DJ,Gutman DA,Cooper LAD

doi

10.1038/s41598-017-15092-3

subject

Has Abstract

pub_date

2017-11-06 00:00:00

pages

14588

issue

1

issn

2045-2322

pii

10.1038/s41598-017-15092-3

journal_volume

7

pub_type

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