  Computer-aided diagnosis has recently received attention for its advantage of
low cost and time efficiency. Although deep learning played a major role in the
recent success of acne detection, there are still several challenges such as
color shift by inconsistent illumination, variation in scales, and high density
distribution. To address these problems, we propose an acne detection network
which consists of three components, specifically: Composite Feature Refinement,
Dynamic Context Enhancement, and Mask-Aware Multi-Attention. First, Composite
Feature Refinement integrates semantic information and fine details to enrich
feature representation, which mitigates the adverse impact of imbalanced
illumination. Then, Dynamic Context Enhancement controls different receptive
fields of multi-scale features for context enhancement to handle scale
variation. Finally, Mask-Aware Multi-Attention detects densely arranged and
small acne by suppressing uninformative regions and highlighting probable acne
regions. Experiments are performed on acne image dataset ACNE04 and natural
image dataset PASCAL VOC 2007. We demonstrate how our method achieves the
state-of-the-art result on ACNE04 and competitive performance with previous
state-of-the-art methods on the PASCAL VOC 2007.
