PATL: Pool-based Active Twin Learner from Oracle with Imitation Learning for Early Epidemic Detection

This project leverages imitation learning, specifically the DAgger algorithm, to detect different epidemic diseases like COVID-19, pneumonia and tuberculosis from various data sources. The goal is to improve the accuracy and robustness of epidemic disease detection models using advanced machine learning techniques.

Table of Contents

• Introduction
• Dataset
• Installation
• Usage
• Model Training
• Evaluation
• Results
• Contributing

Introduction

This project explores the use of imitation learning, specifically the DAgger algorithm, to enhance the detection of epidemic diseases through multi-modal medical imaging, including CT scans and X-rays. The urgent demand for accurate and efficient diagnostic tools during outbreaks has accelerated advancements in this area.

Dataset

The datasets used in this project include:

1. Chest X-ray images from the COVID-19 Radiography Database.
2. CT Scan images from the UCSD-AI4H COVID-CT Dataset.

Installation

To get started, clone the repository:

git clone https://github.com/2ai-lab/PATL.git

Usage

After installation, you can use the notebook file to preprocess data, train models, and evaluate performance:

Model Training

The training process involves using the DAgger algorithm to iteratively refine the model.

Evaluation

Model evaluation is performed on a separate test set. Metrics such as accuracy, precision, recall, and F1-score are computed to assess performance.

Results

The results of the model training and evaluation are stored in the specified directory. Detailed performance metrics and visualizations can be found in the evaluation report.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your changes. Ensure that your code adheres to the project's coding standards and includes appropriate tests.