Deep learning-based classification of dermatological lesions given a limited amount of labeled data.

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Abstract

Artificial intelligence (AI) techniques are promising in early diagnosis of skin diseases. However, a precondition for their success is the access to large-scaled annotated data. Until now, obtaining this data has only been feasible with very high personnel and financial resources.The aim of this study was to overcome the obstacle caused by the scarcity of labeled data.To simulate the scenario of label shortage, we discarded a proportion of labels of the training set. The training set consisted of both labeled and unlabeled images. We then leveraged a self-supervised learning technique to pre-train the AI model on the unlabeled images. Next, we fine-tuned the pre-trained model on the labeled images.When the images in the training dataset were fully labeled, the self-supervised pre-trained model achieved 95.7% of accuracy, 91.7% of precision and 90.7% of sensitivity. When only 10% of the data was labeled, the model could still yield 87.7% of accuracy, 81.7% of precision and 68.6% of sensitivity. In addition, we also empirically verified that the AI model and dermatologists are consistent in visually inspecting the skin images.The experimental results demonstrate the great potential of the self-supervised learning in alleviating the scarcity of annotated data.This article is protected by copyright. All rights reserved.