NaroNet: Objective-based learning of the tumor microenvironment from highly multiplexed immunostained images
Authors/Creators
- 1. Solid Tumors and Biomarkers Program, IDISNA, and Ciberonc, Center for Applied Medical Research, University of Navarra, 31008, Pamplona, Spain
- 2. Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, CA, 94720, Berkeley, USA
- 3. Department of Pathology, Hospital Universitari Arnau de Vilanova, University of Lleida, IRBLleida, Lleida, 25198, Spain
- 4. Department of Pathology, IDISNA, Ciberonc, Clínica Universidad de Navarra, University of Navarra, 31008, Pamplona, Spain
Description
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.
Files
Endometrial_High_grade_cancer.zip
Additional details
Related works
- Cites
- Preprint: arXiv:2103.04617 (arXiv)
- Is cited by
- Preprint: arXiv:2103.05385 (arXiv)