Lung cancer tissue image data utilized for the validation of a fully unsupervised method described in Unsupervised Learning of Contextual Information in Multiplex Immunofluorescence Tissue Cytometry It consists of 16 tissue core adenocarcinomas stained by DAPI counterstain, QKI, BRCA, LUT, CK. For more information please refer to https://ieeexplore.ieee.org/document/9098352 The code implementation is available at https://github.com/djimenezsanchez/Contextual_cell_embeddings

Lung cancer tissue image data utilized for the validation of a fully unsupervised method described in Unsupervised Learning of Contextual Information in Multiplex Immunofluorescence Tissue Cytometry It consists of 16 tissue core adenocarcinomas stained by DAPI counterstain, QKI, BRCA, LUT, CK. For more information please refer to https://ieeexplore.ieee.org/document/9098352 The code implementation is available at https://github.com/djimenezsanchez/Contextual_cell_embeddings

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.

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.

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.