Modelling Airway Geometry as Stock Market Data using Bayesian Changepoint Detection

This repository contains the code for our paper; Modelling Airway Geometry as Stock Market Data using Bayesian Changepoint Detection

The code is written by Kin Quan & Michael Duong

Prerequisites

• Windows
• Matlab R2017b

Usage

A demonstration of the method can found by running the following script

Main_script_for_changepoint_analysis.m

Citation

If you use this code for your research, please cite our paper:

@inproceedings{Quan19,
  title={ Modelling Airway Geometry as Stock Market Data using Bayesian Changepoint Detection},
  author={Quan, Kin and Tanno, Ryutaro and Duong, Michael and Nair, Arjun and Shipley, Rebecca and Jones, Mark and Brereton, Christopher and Hurst, John and Hawkes, David and Jacob, Joseph },
  booktitle={Proceedings of the 10th International Workshop on Machine Learning in Medical Imaging (MLMI 2019)}
  year={2019},
}

Keywords

Changepoint Detection, Bayesian Modelling, Abnormality Detection, Reversible Jump Markov Chain Monte Carlo, Metropolis Hastings & Time Series

Contact

Email: kin.quan.10@ucl.ac.uk

LinkedIn: https://www.linkedin.com/in/kin-quan/

Acknowledgements

• Ryutaro Tanno at University College London, UK & Microsoft Research, Cambridge
• Michael Duong at University College London, UK
• John Hurst at University College London, UK
• David Hawkes at University College London, UK
• Joseph Jacob at University College London, UK