ACNet: Mask-Aware Attention with Dynamic Context Enhancement for Robust Acne Detection
This addresses robust acne detection for medical imaging, but it is incremental as it builds on existing deep learning methods with specific enhancements.
The paper tackled acne detection challenges like inconsistent illumination, scale variation, and high density by proposing ACNet with Composite Feature Refinement, Dynamic Context Enhancement, and Mask-Aware Multi-Attention, achieving state-of-the-art results on ACNE04 and competitive performance on PASCAL VOC 2007.
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.