ASNet: An adaptive scale network for skin lesion segmentation in dermoscopy images

Liangjiu Zhu; Shuanglang Feng; Weifang Zhu; Xinjian Chen

doi:10.1117/12.2549178

28 February 2020 ASNet: An adaptive scale network for skin lesion segmentation in dermoscopy images

Liangjiu Zhu, Shuanglang Feng, Weifang Zhu, Xinjian Chen

Author Affiliations +

Proceedings Volume 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 113170W (2020) https://doi.org/10.1117/12.2549178
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Dermoscopy is a non-invasive dermatology imaging and widely used in dermatology clinic. In order to screen and detect melanoma automatically, skin lesion segmentation in dermoscopy images is of great significance. In this paper, we propose an adaptive scale network (ASNet) for skin lesion segmentation in dermoscopy images. A ResNet34 with pretrained weights is applied as the encoder to extract more representative features. A novel adaptive scale module is designed and inserted into the top of the encoder path to dynamically fuse multi-scale information, which can self-learn based on spatial attention mechanism. Our proposed method is 5-fold cross-validated on a public dataset from Challenge Lesion Boundary Segmentation in ISIC-2018, which includes 2594 images from different types of skin lesion with different resolutions. The Jaccard coefficient, Dice coefficient and Accuracy are 82.15±0.328%, 88.880.390% and 96.00±0.228%, respectively. Experimental results show the effectiveness of the proposed ASNet.

Conference Presentation

Citation Download Citation

Liangjiu Zhu, Shuanglang Feng, Weifang Zhu, and Xinjian Chen "ASNet: An adaptive scale network for skin lesion segmentation in dermoscopy images", Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113170W (28 February 2020); https://doi.org/10.1117/12.2549178

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