Abstract：
:Whole-slide histology images contain information that is valuable for clinical and basic science investigations of cancer, but extracting quantitative measurements from these images is challenging for researchers who are not image analysis specialists. In this paper, we describe HistomicsML2, a software tool for learn-by-example training of machine learning classifiers for histologic patterns in whole-slide images. This tool improves training efficiency and classifier performance by guiding users to the most informative training examples for labeling and can be used to develop classifiers for prospective application or as a rapid annotation tool that is adaptable to different cancer types. HistomicsML2 runs as a containerized server application that provides web-based user interfaces for classifier training, validation, exporting inference results, and collaborative review and can be deployed on GPU servers or cloud platforms. We demonstrate the utility of this tool by using it to classify tumor-infiltrating lymphocytes in breast carcinoma and cutaneous melanoma.
journal_name
Cancer Resjournal_title
Cancer researchauthors
Lee S,Amgad M,Mobadersany P,McCormick M,Pollack BP,Elfandy H,Hussein H,Gutman DA,Cooper LAdoi
10.1158/0008-5472.CAN-20-0668subject
Has Abstractpub_date
2020-12-21 00:00:00eissn
0008-5472issn
1538-7445pii
0008-5472.CAN-20-0668pub_type
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