Radiation Oncology NLP Database 🌎

Welcome to the Radiation Oncology NLP Database! This is the world's first dedicated NLP dataset for radiation oncology, and it covers various NLP tasks to help advance research in this field.

Table of Contents

• Introduction
• Dataset Overview
• Getting Started
• Contributing
• License
• Contact

Introduction

Radiation Oncology NLP Database aims to provide a comprehensive dataset for natural language processing tasks related to radiation oncology. It has been specifically designed to cover a wide range of topics and tasks to enable researchers to develop Radiation-Oncology centered language models and test NLP algorithms/methods on domain-specific data.

Dataset Overview

The dataset covers the following NLP tasks:

1. Logic Reasoning
   Last Update: now

   This task focuses on the ability to reason logically about radiation oncology concepts and cases. It includes complex and multi-step reasoning challenges, as well as simpler deductions.

2. Text Classification
   Last Update: 4 minutes ago

   This task involves classifying text data into predefined categories. It covers various aspects of radiation oncology, such as treatment planning, side effects, and more.

3. Named Entity Recognition (NER)
   Last Update: now

   This task aims to identify and categorize specific entities, such as anatomical structures, radiation doses, and other relevant concepts in radiation oncology.

4. Question and Answering (QA)
   Last Update: now

   This task focuses on the ability to provide concise and accurate answers to questions related to radiation oncology.

5. Text Summarization
   Last Update: now

   This task involves creating concise and informative summaries of lengthy documents and research papers related to radiation oncology.

Getting Started

To get started with the Radiation Oncology NLP Database, follow these steps:

1. Clone the repository git clone https://github.com/zl-liu/Radiation-Oncology-NLP-Database.git

2. Install required packages (optional) pip install -r requirements.txt

3. Explore the dataset and choose the appropriate task for your project

Contributing

We welcome contributions to improve and expand the Radiation Oncology NLP Database. Please follow these guidelines:

1. Fork the project and create a new branch for your changes
2. Make your changes and test them
3. Open a pull request with a clear description of your changes

For more information on how to contribute, check out our CONTRIBUTING.md.

License

This project is licensed under the Apache 2.0 License. See LICENSE.md for more information.

Contact

Please contact zl18864@uga.edu for licensing and other non-technical queries.

If you have any questions, issues, or suggestions, please feel free to reach out to us through GitHub Issues.