Imperial College London

DrDeclanO'Regan

Faculty of MedicineInstitute of Clinical Sciences

Reader in Imaging Sciences
 
 
 
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Contact

 

+44 (0)20 3313 1510declan.oregan

 
 
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Location

 

Imaging Sciences DepartmentHammersmith HospitalHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Biffi:2018:10.1007/978-3-030-00934-2_52,
author = {Biffi, C and Oktay, O and Tarroni, G and Bai, W and De, Marvao A and Doumou, G and Rajchl, M and Bedair, R and Prasad, S and Cook, S and O’Regan, D and Rueckert, D},
doi = {10.1007/978-3-030-00934-2_52},
pages = {464--471},
publisher = {Springer},
title = {Learning interpretable anatomical features through deep generative models: Application to cardiac remodeling},
url = {http://dx.doi.org/10.1007/978-3-030-00934-2_52},
year = {2018}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - Alterations in the geometry and function of the heart define well-established causes of cardiovascular disease. However, current approaches to the diagnosis of cardiovascular diseases often rely on subjective human assessment as well as manual analysis of medical images. Both factors limit the sensitivity in quantifying complex structural and functional phenotypes. Deep learning approaches have recently achieved success for tasks such as classification or segmentation of medical images, but lack interpretability in the feature extraction and decision processes, limiting their value in clinical diagnosis. In this work, we propose a 3D convolutional generative model for automatic classification of images from patients with cardiac diseases associated with structural remodeling. The model leverages interpretable task-specific anatomic patterns learned from 3D segmentations. It further allows to visualise and quantify the learned pathology-specific remodeling patterns in the original input space of the images. This approach yields high accuracy in the categorization of healthy and hypertrophic cardiomyopathy subjects when tested on unseen MR images from our own multi-centre dataset (100%) as well on the ACDC MICCAI 2017 dataset (90%). We believe that the proposed deep learning approach is a promising step towards the development of interpretable classifiers for the medical imaging domain, which may help clinicians to improve diagnostic accuracy and enhance patient risk-stratification.
AU - Biffi,C
AU - Oktay,O
AU - Tarroni,G
AU - Bai,W
AU - De,Marvao A
AU - Doumou,G
AU - Rajchl,M
AU - Bedair,R
AU - Prasad,S
AU - Cook,S
AU - O’Regan,D
AU - Rueckert,D
DO - 10.1007/978-3-030-00934-2_52
EP - 471
PB - Springer
PY - 2018///
SN - 0302-9743
SP - 464
TI - Learning interpretable anatomical features through deep generative models: Application to cardiac remodeling
UR - http://dx.doi.org/10.1007/978-3-030-00934-2_52
UR - http://hdl.handle.net/10044/1/72713
ER -