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Supervised feature learning using convolutional neural networks (CNNs) canprovide concise and disease relevant representations of medical images.However, training CNNs requires annotated image data. Annotating medical imagescan be a time-consuming task and even expert annotations are subject tosubstantial inter- and intra-rater variability. Assessing visual similarity ofimages instead of indicating specific pathologies or estimating diseaseseverity could allow non-experts to participate, help uncover new patterns, andpossibly reduce rater variability. We consider the task of assessing emphysemaextent in chest CT scans. We derive visual similarity triplets from visuallyassessed emphysema extent and learn a low dimensional embedding using CNNs. Weevaluate the networks on 973 images, and show that the CNNs can learn diseaserelevant feature representations from derived similarity triplets. To ourknowledge this is the first medical image application where similarity tripletshas been used to learn a feature representation that can be used for embeddingunseen test images

Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans