  We recognize that the skin lesion diagnosis is an essential and challenging
sub-task in Image classification, in which the Fisher vector (FV) encoding
algorithm and deep convolutional neural network (DCNN) are two of the most
successful techniques. Since the joint use of FV and DCNN has demonstrated
proven success, the joint techniques could have discriminatory power on skin
lesion diagnosis as well. To this hypothesis, we propose the aggregation
algorithm for skin lesion diagnosis that utilize the residual network to
extract the local features and the Fisher vector method to aggregate the local
features to image-level representation. We applied our algorithm on the
International Skin Imaging Collaboration 2018 (ISIC2018) challenge and only
focus on the third task, i.e., the disease classification.
