A Relation Hashing Network Embedded with Prior Features for Skin Lesion Classification

Deep neural networks have become an effective tool for solving end-to-end classification problems and are suitable for many diagnostic settings. However, the success of such deep models often depends on a large number of training samples with annotations. Moreover, deep networks do not leverage the power of domain knowledge which is usually essential for diagnostic decision. Here we propose a novel relation hashing network via meta-learning to address the problem of skin lesion classification with prior features. In particular, we present a deep relation network to capture and memorize the relation among different samples. To employ the prior domain knowledge, we construct the hybrid-prior feature representation via joint meta-learning based on handcrafted models and deep-learned features. In order to utilize the fast and efficient computation of representation learning, we further create a hashing hybrid-prior feature representation by incorporating deep hashing into hybrid-prior representation learning, and then integrating it into our proposed network. Final recognition is obtained from our hashing relation network by learning to compare among the hashing hybrid-prior features of samples. Experimental results on ISIC Skin 2017 dataset demonstrate that our hashing relation network can achieve the state-of-the-art performance for the task of skin lesion classification.