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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2020
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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Strong histopathological relevance patches for MSI vs. MSS classification

Research datakeyboard_double_arrow_right Dataset 29 Feb 2020Publisher:Zenodo
Authors: Yiqing, Shen;

doi: 10.5281/zenodo.3692380 , 10.5281/zenodo.3692379

Strong histopathological relevance patches for MSI vs. MSS classification

Abstract

This is a subset of dataset "Histological images for MSI vs. MSS classification in gastrointestinal cancer, FFPE samples" CRC_DX[1]. For more details, please visit [1]. After a fused distillation, a neural network based framework will vote for the candidates for the histopathological strong relevance patches. We incorporate the morphology knowledge from pathologists to sort out the most representative samples and tag them to this subdata. The dataset has two labels i.e., MSIMUT_strong and MSS_strong. MSIMUT_strong : 28 307 patches. MSS_strong : 27 146 patches. [1] Histological images for MSI vs. MSS classification in gastrointestinal cancer, FFPE samples. https://doi.org/10.5281/zenodo.2530835

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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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