Global and local interpretability for cardiac MRI classification

James R. Clough*, Ilkay Oksuz, Esther Puyol-Antón, Bram Ruijsink, Andrew P. King, Julia A. Schnabel

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

37 Citations (Scopus)

Abstract

Deep learning methods for classifying medical images have demonstrated impressive accuracy in a wide range of tasks but often these models are hard to interpret, limiting their applicability in clinical practice. In this work we introduce a convolutional neural network model for identifying disease in temporal sequences of cardiac MR segmentations which is interpretable in terms of clinically familiar measurements. The model is based around a variational autoencoder, reducing the input into a low-dimensional latent space in which classification occurs. We then use the recently developed ‘concept activation vector’ technique to associate concepts which are diagnostically meaningful (eg. clinical biomarkers such as ‘low left-ventricular ejection fraction’) to certain vectors in the latent space. These concepts are then qualitatively inspected by observing the change in the image domain resulting from interpolations in the latent space in the direction of these vectors. As a result, when the model classifies images it is also capable of providing naturally interpretable concepts relevant to that classification and demonstrating the meaning of those concepts in the image domain. Our approach is demonstrated on the UK Biobank cardiac MRI dataset where we detect the presence of coronary artery disease.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
PublisherSpringer Science and Business Media Deutschland GmbH
Pages656-664
Number of pages9
ISBN (Print)9783030322502
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 13 Oct 201917 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11767 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Country/TerritoryChina
CityShenzhen
Period13/10/1917/10/19

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

Funding

This work was supported by an EPSRC programme Grant (EP/P001009/1) and the Wellcome EPSRC Centre for Medical Engineering at the School of Biomedical Engineering and Imaging Sciences, King’s College London (WT 203148/Z/16/Z). This research has been conducted using the UK Biobank Resource under Application Numbers 40119 and 17806. The GPU used in this research was generously donated by the NVIDIA Corporation.

FundersFunder number
Wellcome EPSRC Centre for Medical Engineering at the School of Biomedical Engineering and Imaging Sciences
King’s College LondonWT 203148/Z/16/Z
Engineering and Physical Sciences Research CouncilEP/P001009/1, EP/N026993/1, EP/M000133/1

    Keywords

    • Cardiac MRI
    • Coronary artery disease
    • Interpretable ML

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