Automatic skin lesion classification based on mid-level feature learning

Dermoscopic images are widely used for melanoma detection. Many existing works based on traditional classification methods and deep learning models have been proposed for automatic skin lesion analysis. The traditional classification methods use hand-crafted features as input. However, due to the strong visual similarity between different classes of skin lesions and complex skin conditions, the hand-crafted features are not discriminative enough and fail in many cases. Recently, deep convolutional neural networks (CNN) have gained popularity since they can automatically learn optimal features during the training phase. Different from existing works, a novel mid-level feature learning method for skin lesion classification task is proposed in this paper. In this method, skin lesion segmentation is first performed to detect the regions of interest (ROI) of skin lesion images. Next, pretrained neural networks including ResNet and DenseNet are used as the feature extractors for the ROI images. Instead of using the extracted features directly as input of classifiers, the proposed method obtains the mid-level feature representations by utilizing the relationships among different image samples based on distance metric learning. The learned feature representation is a soft discriminative descriptor, having more tolerance to the hard samples and hence is more robust to the large intra-class difference and inter-class similarity. Experimental results demonstrate advantages of the proposed mid-level features, and the proposed method obtains state-of-the-art performance compared with the existing CNN based methods.