Multi-level Attentive Skin Lesion Learning for Melanoma Classification.

Melanoma classification plays an important role in skin lesion diagnosis. Nevertheless, melanoma classification is a challenging task, due to the appearance variation of the skin lesions, and the interference of the noises from dermoscopic imaging. In this paper, we propose a multi-level attentive skin lesion learning (MASLL) network to enhance melanoma classification. Specifically, we design a local learning branch with a skin lesion localization (SLL) module to assist the network in learning the lesion features from the region of interest. In addition, we propose a weighted feature integration (WFI) module to fuse the lesion information from the global and local branches, which further enhances the feature discriminative capability of the skin lesions. Experimental results on ISIC 2017 dataset show the effectiveness of the proposed method on melanoma classification.

Citation

Xiaohong Wang, Weimin Huang, Zhongkang Lu, Su Huang. Multi-level Attentive Skin Lesion Learning for Melanoma Classification. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2021 Nov;2021:3924-3927

Mesh Tags

PMID: 34892090

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