A comparison of unsupervised abnormality detection methods for interstitial lung disease

Matt Daykin*, Mathini Sellathurai, Ian Poole

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)
53 Downloads (Pure)


Abnormality detection, also known as outlier detection or novelty detection, seeks to identify data that do not match an expected distribution. In medical imaging, this could be used to find data samples with possible pathology or, more generally, to exclude samples that are normal. This may be done by learning a model of normality, against which new samples are evaluated. In this paper four methods, each representing a different family of techniques, are compared: one-class support vector machine, isolation forest, local outlier factor, and fast-minimum covariance determinant estimator. Each method is evaluated on patches of CT interstitial lung disease where the patches are encoded with one of four embedding methods: principal component analysis, kernel principal component analysis, a flat autoencoder, and a convolutional autoencoder. The data consists of 5500 healthy patches from one patient cohort defining normality, and 2970 patches from a second patient cohort with emphysema, fibrosis, ground glass opacity, and micronodule pathology representing abnormality. From this second cohort 1030 healthy patches are used as an evaluation dataset. Evaluation occurs in both the accuracy (area under the ROC curve) and runtime efficiency. The fast-minimum covariance determinant estimator is demonstrated to have a fair time scaling with dataset dimensionality, while the isolation forest and one-class support vector machine scale well with dimensionality. The one-class support vector machine is the most accurate, closely followed by the isolation forest and fast-minimum covariance determinant estimator. The embeddings from kernel principal component analysis are the most generally useful.

Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis
Subtitle of host publicationMIUA 2018
EditorsMark Nixon, Sasan Mahmoodi, Reyer Zwiggelaar
Number of pages12
ISBN (Electronic)9783319959214
ISBN (Print)9783319959207
Publication statusPublished - 21 Aug 2018
Event22nd Conference on Medical Image Understanding and Analysis 2018 - Southampton, United Kingdom
Duration: 9 Jul 201811 Jul 2018

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


Conference22nd Conference on Medical Image Understanding and Analysis 2018
Abbreviated titleMIUA 2018
Country/TerritoryUnited Kingdom

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)


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