Towards Practical Unsupervised Anomaly Detection on Retinal Images

Towards Practical Unsupervised Anomaly Detection on Retinal Images
Title:
Towards Practical Unsupervised Anomaly Detection on Retinal Images
Other Titles:
DART 2019, MIL3ID 2019: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data
DOI:
10.1007/978-3-030-33391-1_26
Publication Date:
13 October 2019
Citation:
Ouardini K. et al. (2019) Towards Practical Unsupervised Anomaly Detection on Retinal Images. In: Wang Q. et al. (eds) Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data. DART 2019, MIL3ID 2019. Lecture Notes in Computer Science, vol 11795, pp. 225-234, Springer, Cham
Abstract:
Supervised deep learning approaches provide state-of-the-art performance on medical image classification tasks for disease screening. However, these methods require large labeled datasets that involve resource-intensive expert annotation. Further, disease screening applications have low prevalence of abnormal samples; this class imbalance makes the task more akin to anomaly detection. While the machine learning community has proposed unsupervised deep learning methods for anomaly detection, they have yet to be characterized on medical images where normal vs. anomaly distinctions may be more subtle and variable. In this work, we characterize existing unsupervised anomaly detection methods on retinal fundus images, and find that they require significant fine tuning and offer unsatisfactory performance. We thus propose an efficient and effective transfer-learning based approach for unsupervised anomaly detection.Our method employs a deep convolutional neural network trained on ImageNet as a feature extractor, and subsequently feeds the learned feature representations into an existing unsupervised anomaly detection method. We show that our approach significantly outperforms baselines on two natural image datasets and two retinal fundus image datasets, all with minimal fine-tuning. We further show the ability to leverage very small numbers of labelled anomalies to improve performance. Our work establishes a strong unsupervised baseline for image-based anomaly detection, alongside a flexible and scalable approach for screening applications.
License type:
PublisherCopyrights
Funding Info:
This project was supported by funding from the Deep Learning 2.0 program at the Institute for Infocomm Research (I2R), A*STAR, Singapore; research grants from the US National Institutes of Health (NIH grants R01EY19474, P30EY010572, and K12EY027720) and the US National Science Foundation (NSF grants SCH-1622679 and SCH-1622542); unrestricted departmental funding from the Oregon Health Sciences University, and a Career Development Award from Research to Prevent Blindness (New York, NY).
Description:
ISSN:
978-3-030-33390-4
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