Dual attention based network for skin lesion classification with auxiliary learning

Skin lesion varies greatly in appearance, and its classification task suffers from large inter-class similarity and intra-class variation, thus the subtle differences of local pattern contained in the skin lesion regions are critical for its classification. In this paper, we propose a dual attention based network for skin lesion classification with auxiliary learning. The dual attention mechanism includes the spatial attention (SA) and the channel attention (CA) modules. The SA module can focus on the skin lesion region feature with reduced irrelevant artifacts features. In the subsequent CA module, it first captures the non-local based global feature of the skin lesion region and then generates the feature channels reweighting vector, which is used to further refine the meaningful local pattern feature contained in the skin lesion region. Therefore, the performance and the interpretability of the proposed network are enhanced at the same time. The proposed auxiliary learning contains two auxiliary supervision branches and KL regularization. The KL regularization makes the two auxiliary supervision branches collaborate with each other during training through mutual knowledge transferring. The introduced strong regularization can guide the dual attention mechanism to focus on the meaningful local pattern features in a weakly supervised manner and make the network avoid overfitting on small training data. Without extra training data, our proposed network can outperform current competition winners on several datasets, regardless of binary- or multi-classification. The proposed network is robust enough and owns strong interpretability which promotes its clinical application.