Abstract
Dermoscopy imaging analysis is the basic operation for diagnosing and treating skin lesions. Recently, deep neural networks have been able to segment melanoma from surrounding skin accurately. However, because of the limitation of demanding a large amount of floating point operations and having long runtime on skin lesion segmentation network, it is difficult to deploy models to existing medical devices. In this paper, we design LCASA-Net, a novel light-weight neural network architecture, which applies Context-Aware Self-Attention block to effectively and efficiently capture informative features in dermoscopic images. Our model is created specifically for skin lesion segmentation task requiring low latency operation with higher precision. LCASA-Net is up to 2\(\times \) faster, requires 5\(\times \) less FLOPS, possesses 10\(\times \) less parameters and achieves higher performance to existing state-of-the-art methods on ISBI 2017 dataset.
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Ma, D., Wu, H., Sun, J., Yu, C., Liu, L. (2019). A Light-Weight Context-Aware Self-Attention Model for Skin Lesion Segmentation. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11672. Springer, Cham. https://doi.org/10.1007/978-3-030-29894-4_40
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DOI: https://doi.org/10.1007/978-3-030-29894-4_40
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