smart-ml

This repository contains the latest machine learning efforts for automated interpretation of rapid test kits (e.g. LFA).

Within each directory inside this repository, you will find a README.md which lists preliminary information about the directory and its contents. These documentations will often reference various Google Docs documents we've created to supplement the code and explain things in more detail. All of these can be found in the Smart-ML Google Drive folder. More explicitly, the documents include: Master Documentation, Object Detection Training & Evaluation Documentation, Lambda Function Deployment Documentation, Cloud Pipeline Inference Documentation, Cloud Model Deployment Documentation, Cloud Functions Overview.

All test kits images used for base model training, adaptation, and for the drive-through study described in the manuscript titled, Rapidly adaptable interpretation of point-of-care COVID-19 diagnostics, are available at the following repository.

Contents

• classification/: This is the main folder for classification evaluation. classification/README.md outlines the latest model development efforts for classification and it includes system requirements, installation guide, demo, and instructions for use. Please see archive/classification/ for training and deployment. Please contact Jiawei Ma from DVMM in case you have any questions. An ideal setup would be to put the main training code directly under classification. You can update the README after this.

• data/: Placeholder data folder for storing raw and membrane images. NOTE: There are many placeholder data/ folders in this repository, but you can feel free to use this outermost folder for all operations. Alternatively, as different machine learning processes require different types of data, you might use the data/ folder in each component.

• labelling/: This folder contains code and utilities for object-detection bounding-box and polygon mask labelling via the labelme software. NOTE: The folder includes a labelme_requiremenst.txt which has the required packages for labelling and can be used to create a virtual environment.

• lambda-functions/: This folder contains AWS Lambda Function codes, configuration files, AWS Lambda Layers, and individual manufacturer spec files for each test kit. You can also find the same files in AWS -> Console -> Lambda.

• misc/: This folder contains misc documents that can be useful for various tasks.

• object-detection/: This folder contains code and utilities for training and evaluation of, running inference on, and deploying object detection models.

• outlier/: This folder (for now) contains the code for BTNx outlier detection / anomaly detection model we have deployed. Feel free to add other outlier models here if needed.

• s3/: Contains code for downloading logs and folders from S3 for investigating failure cases and checking the logs of our AWS Lambda Functions which contain intermediate image processing outputs.

• sagemaker/: This folder is merely a snapshot of our AWS SageMaker Notebook Instance named deploy-to-aws. Please visit AWS -> Console -> Notebook instances -> deploy-to-aws -> Open Jupyter OR Open JupyterLab to see the complete notebook. Only includes btnx_deploy_and_predict.ipynb for now, which handles the deployment of all related models for the BTNx test kit.

• serverside_testing/: Contains code for testing the serverside locally. With the script inside this folder, you can run batch inputs and get predictions from the cloud model quickly.

• requirements.txt: This is the general requirements file, which contains the Python libraries and packages we have utilized in this project. NOTE: However, please note that it also includes irrelevant libraries and packages. I haven't created a virtual environment (with the exception of labelling/) for this project, and this file includes all of the packages I've installed for various reasons. Feel free to dig into this deeper and remove unnecessary packages. Nevertheless, you can run pip install -r requirements.txt to install all of these packages.