This code is for the REACT2 project and related paper "Machine Learning to Support Visual Auditing of Home-based Lateral Flow Immunoassay Self-Test Results for SARS-CoV-2 Antibodies" [Communications Medicine 2022].
The project can be divied into two major parts: segmentation and classification. The code and details are provided in each sub-folder. The pretrained models of both segmentation and classification tasks can be downloaded from the releases page.
Please Note: Porting of the ALFA pipeline described in the paper to this Git repository is in progress. It will take time as ALFA was developed for a specific batch processing system on a specific university cluster.
If you have further questions, please contact me.
To cite this code for publications - please use:
@article{wong2022machine,
title={Machine learning to support visual auditing of home-based lateral flow immunoassay self-test results for SARS-CoV-2 antibodies},
author={Wong, Nathan CK and Meshkinfamfard, Sepehr and Turb{\'e}, Val{\'e}rian and Whitaker, Matthew and Moshe, Maya and Bardanzellu, Alessia and Dai, Tianhong and Pignatelli, Eduardo and Barclay, Wendy and Darzi, Ara and others},
journal={Communications Medicine},
volume={2},
number={1},
pages={1--10},
year={2022},
publisher={Nature Publishing Group}
}