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Abstract <p>Cutaneous malignancies represent one of the most common cancers globally, with consistently rising incidence rates driving demand for enhanced diagnostic methodologies. While dermoscopy delivers high-resolution image data, existing CNN (convolutional neural network)-based approaches display constrained perception abilities when handling complex lesion boundaries and frequency-domain features. To overcome these constraints, we introduce EASNet, an innovative edge-aware segmentation network that combines frequency-domain insights with explicit boundary modeling. EASNet leverages discrete cosine transform (DCT) and discrete wavelet transform (DWT) to acquire multi-scale frequency information, maintaining global structures alongside precise boundary details. Furthermore, a boundary-driven criss-cross (BDCC) attention component strengthens spatial dependency learning, and a hybrid loss mechanism guarantees accurate boundary supervision throughout training. Extensive experiments on ISIC2017 and ISIC2018 datasets reveal that EASNet attains competitive performance in segmentation precision, boundary clarity, and positional consistency. This work pushes forward dermatological image analysis, offering a dependable tool for precise clinical assessment and therapeutic strategy development.</p> Graphical Abstract <p></p>

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EASNet: Edge-aware Segmentation Network for Skin Lesion Segmentation with Boundary-aware and Frequency Attention Mechanisms

  • 期刊论文

  • 发表日期：

    2025年12月12日

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  • Junwei Yu,
  • Yuhe Xia,
  • Jianping Li,
  • Nan Liu,
  • Haoze Li,
  • Weiya Shi

摘要

Abstract

Cutaneous malignancies represent one of the most common cancers globally, with consistently rising incidence rates driving demand for enhanced diagnostic methodologies. While dermoscopy delivers high-resolution image data, existing CNN (convolutional neural network)-based approaches display constrained perception abilities when handling complex lesion boundaries and frequency-domain features. To overcome these constraints, we introduce EASNet, an innovative edge-aware segmentation network that combines frequency-domain insights with explicit boundary modeling. EASNet leverages discrete cosine transform (DCT) and discrete wavelet transform (DWT) to acquire multi-scale frequency information, maintaining global structures alongside precise boundary details. Furthermore, a boundary-driven criss-cross (BDCC) attention component strengthens spatial dependency learning, and a hybrid loss mechanism guarantees accurate boundary supervision throughout training. Extensive experiments on ISIC2017 and ISIC2018 datasets reveal that EASNet attains competitive performance in segmentation precision, boundary clarity, and positional consistency. This work pushes forward dermatological image analysis, offering a dependable tool for precise clinical assessment and therapeutic strategy development.

Graphical Abstract