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.
The best performances recorded during this challenge have been obtained using fully-supervised deep learning architectures (AlexNet @cite_1 ), and transfer learning (VGG-16 nets @cite_19 , ResNet @cite_9 ) with networks previously trained on ImageNet. While the success of approaches introduced by @cite_19 and @cite_9 highlight the benefits of using additional data to improve performance, the additional data they use is different to the skin lesion data. This because these approaches, @cite_19 , @cite_9 are fully supervised and therefore can only make use of labelled data, the authors were therefore limited to use datasets for which labelled data was available.
@cite_19 @cite_9 @cite_1
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Malignant melanoma has one of the most rapidly increasing incidences in the world and has a considerable mortality rate. Early diagnosis is particularly important since melanoma can be cured with prompt excision. Dermoscopy images play an important role in the non-invasive early detection of melanoma [1]. However, melanoma detection using human vision alone can be subjective, inaccurate and poorly reproducible even among experienced dermatologists. This is attributed to the challenges in interpreting images with diverse characteristics including lesions of varying sizes and shapes, lesions that may have fuzzy boundaries, different skin colors and the presence of hair [2]. Therefore, the automatic analysis of dermoscopy images is a valuable aid for clinical decision making and for image-based diagnosis to identify diseases such as melanoma [1-4]. Deep residual networks (ResNets) has achieved state-of-the-art results in image classification and detection related problems [5-8]. In this ISIC 2017 skin lesion analysis challenge [9], we propose to exploit the deep ResNets for robust visual features learning and representations. Deep learning methods for image analysis have shown impressive performance in recent years. In this paper, we present deep learning based approaches to solve two problems in skin lesion analysis using a dermoscopic image containing skin tumor. In the first problem, we use a fully convolutional-deconvolutional architecture to automatically segment skin tumor from the surrounding skin. In the second problem, we use a simple convolutional neural network and VGG-16 architecture using transfer learning to address the two different tasks in skin tumor classification. The proposed models are trained and evaluated on standard benchmark datasets from the International Skin Imaging Collaboration (ISIC) 2017 Challenge, which consists of 2000 training samples and 600 testing samples. The result shows that the proposed methods achieve promising performances. In the first problem, the average value of Jaccard index for lesion segmentation using fully convolutional-deconvolutional architecture is 0.507. In the second problem, the values of area under the receiver operating characteristic curve (AUC) on two different lesion classifications using VGG16 with transfer learning are 0.763 and 0.869, respectively; the average value of AUC in two tasks is 0.816.