  Accurate and unbiased examinations of skin lesions are critical for the early
diagnosis and treatment of skin diseases. Visual features of skin lesions vary
significantly because the images are collected from patients with different
lesion colours and morphologies by using dissimilar imaging equipment. Recent
studies have reported that ensembled convolutional neural networks (CNNs) are
practical to classify the images for early diagnosis of skin disorders.
However, the practical use of these ensembled CNNs is limited as these networks
are heavyweight and inadequate for processing contextual information. Although
lightweight networks (e.g., MobileNetV3 and EfficientNet) were developed to
achieve parameters reduction for implementing deep neural networks on mobile
devices, insufficient depth of feature representation restricts the
performance. To address the existing limitations, we develop a new lite and
effective neural network, namely HierAttn. The HierAttn applies a novel deep
supervision strategy to learn the local and global features by using
multi-stage and multi-branch attention mechanisms with only one training loss.
The efficacy of HierAttn was evaluated by using the dermoscopy images dataset
ISIC2019 and smartphone photos dataset PAD-UFES-20 (PAD2020). The experimental
results show that HierAttn achieves the best accuracy and area under the curve
(AUC) among the state-of-the-art lightweight networks. The code is available at
https://github.com/anthonyweidai/HierAttn.
