/tuberculosis-svr

Official implementation of paper: Tuberculosis severity estimation from volumetric CT scans using uniformizing techniques and 3D Convolutional Net.

Primary LanguageJupyter Notebook

Codebase for TITLE submitted to ECIR 2020.

This work is evaluated on CLEF evaluation: ImageCLEF 2019 Tuberculosis - Severity scoring challenge.

This repository contains the codes and scripts used in the paper titled: "ADD TITLE". The challenge was dedicated to the analysis of 3D Computed Tomography (CT) image data of tuberculosis (TB) patients.

Usage

This work is implemented in Python 3.6 and Keras using Tensorflow as backend.

Dependencies

Tested code using:

  • Ubuntu 14.04
  • Windows 8
  • Python 3.6

Directory Structure & Usage

  • main: Contains codes to final submission
  • utils: Contains helper codes to preprocess and visualize samples in dataset.

This work 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.