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MSCA-Net: Multi-scale contextual attention network for skin lesion segmentation
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AI
DOI：10.1016/j.patcog.2023.109524.png)
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Abstract
En 中文
Lesion segmentation algorithms automatically outline lesion areas in medical images, facilitating more effective identification and assessment of the clinically relevant features, and improving the efficacy and diagnosis accuracy. However, most fully convolutional network based segmentation methods suf-fer from spatial and contextual information loss when decreasing image resolution. To overcome this shortcoming, this paper proposes a skin lesion segmentation model, namely, the Multi-Scale Contex-tual Attention Network (MSCA-Net), which can exploit the multi-scale contextual information in im-ages. Inspired by the skip connection of U-Net, we design a multi-scale bridge (MSB) module which interacts with multi-scale features to effectively fuse the multi-scale contextual information of the en-coder and decoder path features. We further propose a global-local channel spatial attention module (GL-CSAM), aiming at capturing global contextual information. In addition, to take full advantage of the multi-scale features of the decoder, we propose a scale-aware deep supervision (SADS) module to achieve hierarchical iterative deep supervision. Comprehensive experimental results on the public dataset of ISIC 2017, ISIC 2018, and PH2 show that our proposed method outperforms other state-of-the-art methods, demonstrating the efficacy of our method in skin lesion segmentation. Our code is available at https://github.com/YonghengSun1997/MSCA-Net . (c) 2023 Published by Elsevier Ltd.
Keywords:
Skin lesion segmentation
Multi -scale bridge module
Global -local channel spatial attention
module
Scale -aware deep supervision module
Journal
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7.6
Papers:
1.3W
Citations:
4.5W
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