Few-shot learning for skin lesion image classification

The mortality of skin pigmented malignant lesions is very high, especially melanoma. Due to the limitation of marking means, the large-scale annotation data of skin lesions are generally more difficult to obtain. When the deep learning model is trained on a small dataset, its generalization performance is limited. Using prior knowledge to expand small sample data is a general model method of learning classification, which is difficult to deal with complex skin problems. On the basis of a small amount of labeled skin lesion image data, this paper uses the improved Relational Network for measurement learning to realize the classification of skin disease. This method uses relative position network (RPN) and relative mapping network (RMN), in which RPN captures and extracts feature information by attention mechanism, and RMN obtains the similarity of image classification by weighted sum of attention mapping distance. The average accuracy of classification is 85% on the public ISIC melanoma dataset, and the results show the effectiveness and applicability of the method.