Abstract
In this paper, we propose a dense-residual attention network for skin lesion segmentation. The proposed network is end-to-end and doesn’t need any post-processing operations or pretrained weights to fine-tune. Specifically, we propose the dense-residual block in our network to deal with the problem of fixed receptive field and meanwhile ease the gradient vanishing problem (as often occurred in convolution neural networks). Moreover, an attention gate is designed to enhance the network discriminative ability and ensure the efficiency of feature learning. During the network training, we introduce a novel loss function based on the jaccard distance to tackle the class imbalance issue in medical datasets. The proposed network achieves the state-of-the-art performance on the benchmark ISIC 2017 Challenge dataset without any external training samples. Experimental results show the effectiveness of our dense-residual attention network.
Keywords
This work is supported by the Guangdong Provincial Science and Technology Project (2017B010110005), the Shenzhen Science and Technology Project under Grant (JCYJ20170817161916238, GGFW2017040714161462).
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Song, L., Lin, J., Wang, Z.J., Wang, H. (2019). Dense-Residual Attention Network for Skin Lesion Segmentation. 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_37
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DOI: https://doi.org/10.1007/978-3-030-32692-0_37
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