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 Repjournal_title
Scientific reportsauthors
Nalisnik M,Amgad M,Lee S,Halani SH,Velazquez Vega JE,Brat DJ,Gutman DA,Cooper LADdoi
10.1038/s41598-017-15092-3subject
Has Abstractpub_date
2017-11-06 00:00:00pages
14588issue
1issn
2045-2322pii
10.1038/s41598-017-15092-3journal_volume
7pub_type
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