  The accurate detection of lesion attributes is meaningful for both the
computeraid diagnosis system and dermatologists decisions. However, unlike
lesion segmentation and melenoma classification, there are few deep learning
methods and literatures focusing on this task. Currently, the lesion attribute
detection still remains challenging due to the extremely unbalanced class
distribution and insufficient samples, as well as large intraclass and low
interclass variations. To solve these problems, we propose a deep learning
framework named superpixel attention network (SANet). Firstly, we segment input
images into small regions and shuffle the obtained regions by the random
shuttle mechanism (RSM). Secondly, we apply the SANet to capture discriminative
features and reconstruct input images. Specifically, SANet contains two sub
modules: superpixel average pooling and superpixel at tention module. We
introduce a superpixel average pooling to reformulate the superpixel
classification problem as a superpixel segmentation problem and a SAMis
utilized to focus on discriminative superpixel regions and feature channels.
Finally, we design a novel but effective loss, namely global balancing loss to
address the serious data imbalance in ISIC 2018 Task 2 lesion attributes
detection dataset. The proposed method achieves quite good performance on the
ISIC 2018 Task 2 challenge.
