This is the official implementation (Tensorflow and MATLAB) of CellEmbeddings:
Unsupervised Learning of Contextual Information in Multiplex Immunofluorescence Tissue Cytometry
Daniel Jiménez-Sánchez, Mikel Ariz, Carlos Ortiz-de-Solórzano. Paper.
New machine learning models designed to capture the histopathology of tissues should account not only for the phenotype and morphology of the cells but also learn complex spatial relationships between them. To achieve this, we represent the tissue as an interconnected graph, where previously segmented cells become nodes of the graph. Then the relationships between cells are learned and embedded into a low-dimensional vector, using a Graph Neural Network. CellEmbeddings is a fully unsupervised method that learns how to optimally encode cell phenotypes, morphologies, and cell-to-cell interactions from histological tissues labeled using multiplex immunohistochemistry. This framework solves limitations of previous image understanding methods whose data representation strategies are either inappropriate for Machine Learning or do not properly capture the interaction between cells. This may render next-generation image understanding deep learning frameworks predicting clinically relevant patient-level labels.
To replicate the paper's experiments on lung cancer multiplexed data, first download the images following the link. Then, add the images to the folder 'Images/Original/'.
This package is compatible with Windows. You'll need a cuda-enabled GPU in your computer system to run CellEmbeddings, visit this tutorial. You can check that your cuda is installed using the nvidia-smi command. Then, follow the instructions:
- Install conda (https://www.anaconda.com/products/individual)
- Create a conda environment using the following command:
conda create --name tf_gpu tensorflow-gpu==1.15 networkx==1.11 joblib
Execute main.m
If you make use of this code in your own work, please cite our paper:
@inproceedings{jimenez2020unsupervised,
title={Unsupervised Learning of Contextual Information in Multiplex Immunofluorescence Tissue Cytometry},
author={Jiménez-Sánchez, Daniel and Ariz, Mikel and Ortiz-de-Solórzano, Carlos},
booktitle={2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)},
pages={1275--1279},
year={2020},
organization={IEEE}
}