Multiscale Attention U-Net for Skin Lesion Segmentation

Skin cancer is the most common type of cancer in the world and it is more treatable if diagnosed early. The diagnosis process usually starts with segmenting the skin lesion area and planning a follow-up treatment by the dermatologists. Thus, the segmentation process plays a critical role in the trea...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.59145-59154
Main Author: Alahmadi, Mohammad D.
Format: Article
Language:English
Subjects:
Artificial neural networks
Attention mechanism
Cancer
Datasets
Decoding
deep learning
Image segmentation
Lesions
Machine learning
Melanoma
Representations
Segmentation
Semantics
Skin
skin cancer
Task analysis
U-net
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Summary:Skin cancer is the most common type of cancer in the world and it is more treatable if diagnosed early. The diagnosis process usually starts with segmenting the skin lesion area and planning a follow-up treatment by the dermatologists. Thus, the segmentation process plays a critical role in the treatment process. In recent years, machine learning methods, especially deep convolutional neural networks are proposed to address the segmentation challenge. The common segmentation methods (e.g., U-Net) deploy a series of encoding blocks to model the local representation and subsequently a series of decoding blocks to capture the semantic relation. However, these structures are usually limited to model multi-scale objects with large variations in texture and shape. To address these limitations, we propose a Multi-Scale Attention U-Net (MSAU-Net) for skin lesion segmentation. In particular, we improve the typical U-net by inserting an attention mechanism at the bottleneck of the network to model the hierarchical representation. The attention module aggregates the multi-level representation in a non-linear fashion to selectively adjust the representative features. Then it deploys a Bidirectional Convolutional Long Short-term Memory (BDC-LSTM) structure to fetch the common discriminative features and suppress the less informative ones. We incorporate the resulted features in each block of the decoding path to highlight the important regions. We have evaluated our proposed network in three public skin lesion datasets, including ISIC 2017, ISIC 2018, and PH2 datasets. The experimental results demonstrate that the proposed pipeline outperforms the existing alternatives.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3179390