uniformizing-3D
uniformizing-3D copied to clipboard
[MICCAI'2020 PRIME] Uniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Severity Estimation.
Intro
This is official code of MICCAI'2020 PRIME workshop paper:
Uniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Prediction (Paper, arXiv)
Virtual Presentation at MICCAI'2020 PRIME
Citation
If you use this code or models in your scientific work, please cite the following paper:
@inproceedings{zunair2020uniformizing,
title={Uniformizing Techniques to Process CT Scans with 3D CNNs for Tuberculosis Prediction},
author={Zunair, Hasib and Rahman, Aimon and Mohammed, Nabeel and Cohen, Joseph Paul},
booktitle={International Workshop on PRedictive Intelligence In MEdicine},
pages={156--168},
year={2020},
organization={Springer}
}
Data Processing Method
Data uniformizing methods
3D Convolutional Neural Network
Results
Dependencies
- Ubuntu 14.04
- Python 3.6
- Tensorflow: 2.0.0
- Keras: 2.3.1
Environment setup
You can create the appropriate conda environment by running
conda env create -f environment.yml
Directory Structure & Usage
First, get the data from here. Then:
- Run notebooks in order
-
others
: Contains helper codes to preprocess and visualize samples in dataset.
Demo
A 🤗 Spaces demo for detecting pneumonia from CT scans using our method is available here. Demo built by Faizan Shaikh.
This is an extension of previous work
More details at this link
Zunair, H., Rahman, A., Mohammed, N.: Estimating Severity from CT Scans
of Tuberculosis Patients using 3D Convolutional Nets and Slice Selection. In:
CLEF2019 Working Notes. Volume 2380 of CEUR Workshop Proceedings.,
Lugano, Switzerland, CEUR-WS.org
<http://ceur-ws.org/Vol-2380>(September 9-12 2019)
Previous paper published in CEUR-WS. Paper can be found at CLEF Working Notes 2019 under the section ImageCLEF - Multimedia Retrieval in CLEF.
License
Your driver's license.