Deeply Supervised Skin Lesions Diagnosis with Stage and Branch Attention
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
---|---|
Pages (from-to) | 719-729 |
Journal / Publication | IEEE Journal of Biomedical and Health Informatics |
Volume | 28 |
Issue number | 2 |
Online published | 25 Aug 2023 |
Publication status | Published - Feb 2024 |
Link(s)
Abstract
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 parameter 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. © 2023 IEEE.
Research Area(s)
- Branch attention, Computational modeling, Convolution, Deep supervision, Feature extraction, Lesions, Skin, Skin lesions classification, Stage attention, Training, Transformers, Vision transformer, Attention, disease classification, skin lesion
Citation Format(s)
Deeply Supervised Skin Lesions Diagnosis with Stage and Branch Attention. / Dai, Wei; Liu, Rui; Wu, Tianyi et al.
In: IEEE Journal of Biomedical and Health Informatics, Vol. 28, No. 2, 02.2024, p. 719-729.
In: IEEE Journal of Biomedical and Health Informatics, Vol. 28, No. 2, 02.2024, p. 719-729.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review