Denoising adversarial autoencoders: classifying skin lesions using limited labelled training data. Issue 8 (12th September 2018)

• Record Type: Journal Article
• Authors: Creswell, Antonia; Pouplin, Alison; Bharath, Anil A.
• Is Part Of: IET computer vision. Volume 12:Issue 8(2018)
• Page Start: 1105
• Page End: 1111
• Publication Date: 2018-09-12
• Subjects: pattern classification -- feature extraction -- skin -- medical image processing -- image classification -- learning (artificial intelligence); adversarial autoencoder -- skin lesions -- labelled training data -- deep learning model -- classifying medical images -- unlabelled medical data -- labelled data -- specific case -- vast amounts -- unlabelled data -- class labels -- learned representation -- denoising components -- model yields superior classification performance; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2018.5243
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : The authors propose a novel deep learning model for classifying medical images in the setting where there is a large amount of unlabelled medical data available, but the amount of labelled data is limited. They consider the specific case of classifying skin lesions as either benign or malignant. In this setting, the authors' proposed approach – the semi‐supervised, denoising adversarial autoencoder – is able to utilise vast amounts of unlabelled data to learn a representation for skin lesions, and small amounts of labelled data to assign class labels based on the learned representation. They perform an ablation study to analyse the contributions of both the adversarial and denoising components and compare their work with state‐of‐the‐art results. They find that their model yields superior classification performance, especially when evaluating their model at high sensitivity values.