uniformizing-3D

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.