mlsanityData to 14 Oct 2026

Automated skin lesion segmentation using multi-scale feature extraction scheme and dual-attention mechanism

arXiv 2111.08708 ↗First posted 16 Nov 2021eess.IV cs.CVcited by 1

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Abstract (arXiv)

Segmenting skin lesions from dermoscopic images is essential for diagnosing skin cancer. But the automatic segmentation of these lesions is complicated due to the poor contrast between the background and the lesion, image artifacts, and unclear lesion boundaries. In this work, we present a deep learning model for the segmentation of skin lesions from dermoscopic images. To deal with the challenges of skin lesion characteristics, we designed a multi-scale feature extraction module for extracting the discriminative features. Further in this work, two attention mechanisms are developed to refine the post-upsampled features and the features extracted by the encoder. This model is evaluated using the ISIC2018 and ISBI2017 datasets. The proposed model outperformed all the existing works and the top-ranked models in two competitions.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 52 references that are in the map. First posted on arXiv 16 Nov 2021.

Builds on

22 of 52 references.

References · 22

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10 more
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Cited by year

Citing papers per year: 2022: 1.

1 paper.

  1. 2022A survey, review, and future trends of skin lesion segmentation and classificationcited by 21