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Theory and design of a modular classification framework: Application to colorectal tumor detection

descriptionPublicationkeyboard_double_arrow_right Article 16 May 2019Publisher:Zenodo
Authors: Thomas E. Tavolara; M. Khalid Khan Niazi; Vidya Arole; Wei Chen; Wendy Frankel; Metin N. Gurcan;

doi: 10.5281/zenodo.3377909 , 10.5281/zenodo.2874385 , 10.5281/zenodo.2874386 , 10.5281/zenodo.3377815

Theory and design of a modular classification framework: Application to colorectal tumor detection

Abstract

Each 'set' contains tiles sampled from 6 colorectal tumor biopsies, totaling 24 slides. To build the cross-validation scheme used in the paper, simply use three sets for training and left out set for validation. In addition, 50 high power fields from 25 external set of slides are included for testing purposes.

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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