Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data
This addresses a domain-specific problem in medical imaging for healthcare applications, but it is incremental as it builds on existing autoencoder and adversarial methods.
The paper tackles the problem of classifying skin lesions as malignant or benign with limited labelled data by proposing a semi-supervised denoising adversarial autoencoder that leverages unlabelled data for representation learning, achieving superior classification performance in this setting.
We 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 labelled data is in limited supply. We consider the specific case of classifying skin lesions as either malignant or benign. In this setting, the 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. We analyse the contributions of both the adversarial and denoising components of the model and find that the combination yields superior classification performance in the setting of limited labelled training data.