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

Year: 2021
Type: dataset
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.
DOI: 10.5281/zenodo.4596337
Authors: Jiménez-Sánchez, Daniel, Ariz, Mikel, Chang, Hang, Matias-Guiu, Xavier, Andrea, Carlos E. De, Ortiz-De-Solórzano, Carlos
Institutions: Navarre Institute of Health Research, Centro de Investigación Médica Aplicada, Universidad de Navarra, Lawrence Berkeley National Laboratory, Universitat de Lleida, Hospital Universitari Arnau de Vilanova, Instituto de Investigación Biomédica de Lleida, Clinica Universidad de Navarra
Language: English
Open Access status: green
OpenAlex ID: W4393567308
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