MHorUNet: High-order spatial interaction UNet for skin lesion segmentation

• Published in: Biomedical signal processing and control 2024-02, Vol.88, p.105517, Article 105517
• Main Authors: Wu, Renkai, Liang, Pengchen, Huang, Xuan, Shi, Liu, Gu, Yuandong, Zhu, Haiqin, Chang, Qing
• Format: Article
• Language: English
• Subjects: Computer-aided diagnosis; Deep learning; Skin lesion segmentation
• ISSN: 1746-8094
• DOI: 10.1016/j.bspc.2023.105517

In recent years, dermoscopy, as a noninvasive means of detection, has been increasingly used in the auxiliary diagnosis of skin disease, especially for skin cancer, such as malignant melanoma. And the automatic segment is a key step to improve accuracy of diagnosis. Generally, UNet models and its alternative schemes have occupied the vast majority of segmentation tasks in medical image processing. However, many of the current models are not perfect, the ordinary convolution in the UNet model cannot exhibit spatial dependence and remote interaction, while the use of Transformers as a convolution alternative is gradually becoming mainstream, but there are problems such as large data volume requirements as well as high computational effort in dealing with medical clinical problems. Therefore, we propose a HorUNet model with higher-order spatial interaction based on recursive gate convolution, and add a multi-stage dimensional fusion mechanism to the skip connection part to form the MHorUNet model architecture. The higher-order interaction mechanism with recursive gate convolution not only has the key factors for the success of Transformers, but also retains the excellent characteristics of convolution itself. We first performed comparative experiments by performing in two typical public skin lesion datasets (ISIC2017 and ISIC2018) and then used the PH2 dataset and our own dataset as external validation. The experimental results show that our method performs best in several metrics. This confirms that our model has a better generalization capability in terms of medically accurate segmentation results with high segmentation accuracy. The code can be obtained from https://github.com/wurenkai/MHorUNet. •The UNet model architecture introduces higher-order spatial interaction mechanisms.•A combination of multi-level fusion mechanisms and higher-order interactions.•Best performance in multiple skin lesion public datasets.•External validation in our dataset (skin color yellow) is optimal.