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Dataset . 2021
License: CC BY
Data sources: Datacite
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Dataset . 2021
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Dataset . 2021
Data sources: Datacite
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Dataset . 2021
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NaroNet: Objective-based learning of the tumor microenvironment from highly multiplexed immunostained images

Authors: Daniel Jiménez-Sánchez; Mikel Ariz; Hang Chang; Xavier Matias-Guiu; Carlos E. de Andrea; Carlos Ortiz-de-Solórzano;

NaroNet: Objective-based learning of the tumor microenvironment from highly multiplexed immunostained images

Abstract

All data supporting the findings of the publication "NaroNet: Objective-based learning of the tumor microenvironment from highly multiplexed immunostained images", including multiplex images, ground-truth masks, and patient data. The code used to produce the results of this study is available at https://github.com/djimenezsanchez/NaroNet Synthetic_experiments.zip. contains synthetic patient cohorts generated from the synthetic tissue simulator called Synplex. Synplex tissue generator is thoroughly described in the arxiv preprint https://arxiv.org/abs/2103.04617 Endometrial_High_grade_cancer.zip contains tiff stacks and patient data for the endometrial high-grade cancer cohort. Note that the endometrial high-grade cancer images provided are already unmixed.

Keywords

Artificial intelligence, Tumor microenvironment, Multiplex image analysis, Computer vision

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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.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Cancer Research