Abstract
Automatic segmentation of skin lesion in dermatoscope images is important for clinic diagnosis and assessment of melanoma. However, due to the large variations of scale, shape and appearance of the lesion area, accurate and automatic segmentation of skin lesion is faced with great challenges. In this paper, we first introduce the pyramid attention module for global multi-scale features aggregation. The module selectively integrates different multi-scale features associated with lesion by optimizing the features of each scale and suppressing the irrelevant noise. Based on this module, we propose an automatic framework for skin lesion segmentation. In addition, the widely used loss function based on dice coefficient is independent of the relative size of the segmented target, which leads to the insufficient attention of the network to small-scale samples. Therefore, we propose a new loss function based on scale-attention to effectively balance the weight of attention of the network to samples with different scales and improve the segmentation accuracy of small-scale samples. The robustness of the proposed method was evaluated on two public databases: ISIC 2017 and 2018 for skin lesion analysis towards melanoma detection challenge and it could prove that the proposed method could considerably improve the performance of skin lesion segmentation and achieve the state-of-the-art results on ISIC 2017.
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Wang, H., Wang, G., Sheng, Z., Zhang, S. (2019). Automated Segmentation of Skin Lesion Based on Pyramid Attention Network. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_50
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DOI: https://doi.org/10.1007/978-3-030-32692-0_50
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