Abstract
The number of skin cancer cases worldwide is increasing by millions every year. A large number of patients bring great pressure to the diagnosis and treatment of skin cancer, it is urgent to apply automatic segmentation techniques to skin lesions to help the diagnosis of skin lesions and the evaluation of recovery. At present, there are still challenges in automatic skin lesion segmentation, including blurring irregular lesion boundaries, low contrast between the lesion and surrounding skin, and all kinds of interference with bubbles, lights, and hairs. We found that modeling the context relationship by using the strongest consistent masked global context can focus only on the lesion region with a high degree. Based on the observation, we propose an approximated masked global context network (AMGC-Net), which firstly approximates the masked global context by constructing the approximated masked global context, and calculates the similarity between each pixel and the approximated masked global information at the spatial level to form a global context requirements gating coefficient matrix, and then captures the dependencies between channels at the channel level to improve segmentation performance. The AMGC-Net is assessed on three public skin challenge datasets: PH2, ISBI2016, and ISIC2018. It achieves state-of-the-art results when compared to some new methods in terms of sensitivity.
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References
APAAl-Masni, M.A., Al-Antari, M.A., Choi, M.T., Han, S.M., Kim, T.S.: Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks. Comput. Methods Prog. Biomed. 162, 221–231 (2018)
Bi, L., Kim, J., Ahn, E., Kumar, A., Feng, D., Fulham, M.: Step-wise integration of deep class-specific learning for dermoscopic image segmentation. Pattern Recogn. 85, 78–89 (2019)
Cao, Y., Xu, J., Lin, S., Wei, F., Hu, H.: Gcnet: non-local networks meet squeeze-excitation networks and beyond. In: ICCVW (2019)
Fu, J., et al.: Adaptive context network for scene parsing. In: ICCV, pp. 6748–6757 (2019)
Hasan, M.K., Dahal, L., Samarakoon, P.N., Tushar, F.I., Martí, R.: DSNet: automatic dermoscopic skin lesion segmentation. Comput. Biol. Med. 120, 103738 (2020)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: CVPR, pp. 7132–7141 (2018)
Jiang, Y., Cao, S., Tao, S., Zhang, H.: Skin lesion segmentation based on multi-scale attention convolutional neural network. IEEE Access 8, 122811–122825 (2020)
Li, X., Zhao, H., Han, L., Tong, Y., Tan, S., Yang, K.: Gated fully fusion for semantic segmentation. AAAI 34(7), 11418–11425 (2020)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: ICCV, pp. 2980–2988 (2017)
Lin, G., Milan, A., Shen, C., Reid, I.: Refinenet: multi-path refinement networks for high-resolution semantic segmentation. In: CVPR, pp. 1925–1934 (2017)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)
Mishra, R., Daescu, O.: AlgoDerm: an end-to-end mobile application for skin lesion analysis and tracking. In: Proceedings of the International Conference on Medical and Health Informatics System, pp. 3–9 (2019)
Oktay, O., et al.: Attention u-net: learning where to look for the pancreas (2018). https://arxiv.org/abs/1804.03999
Rogers, H.W., Weinstock, M.A., Feldman, S.R., Coldiron, B.M.: Incidence estimate of nonmelanoma skin cancer (keratinocyte carcinomas) in the US population (2012. JAMA Dermatol. 151(10), 1081–1086 (2015)
Singh, V.K., et al.: FCA-Net: adversarial learning for skin lesion segmentation based on multi-scale features and factorized channel attention. IEEE Access 7, 130552–130565 (2019)
Tang, Y., Fang, Z., Yuan, S., Xing, Y., Zhou, J.T., Yang, F.: iMSCGnet: iterative multi-scale context-guided segmentation of skin lesion in dermoscopic images. IEEE Access 8, 39700–39712 (2020)
Tran, S.T., Cheng, C.H., Nguyen, T.T., Le, M.H., Liu, D.G.: TMD-Unet: triple-unet with multi-scale input features and dense skip connection for medical image segmentation. Healthc. Multidiscip. Digital Publ. Inst. 9(1), 54 (2021)
Tu, W., et al.: Segmentation of lesion in dermoscopy images using dense-residual network with adversarial learning. In: ICIP, pp. 1430–1434 (2019)
Wang, Y., Wei, Y., Qian, X., Zhu, L., Yang, Y.: DONet: dual objective networks for skin lesion segmentation (2020). https://arxiv.org/abs/2008.08278
Wang, Z., Zou, N., Shen, D., Ji, S.: Non-local U-nets for biomedical image segmentation. In: AAAI, pp. 6315–6322 (2020)
Wang, R., Chen, S., Fan, J., Li, Y.: Cascaded context enhancement for automated skin lesion segmentation (2020). https://arxiv.org/abs/2004.08107
Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: BiSeNet: bilateral segmentation network for real-time semantic segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 334–349. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8_20
Zhang, H., et al.: Context encoding for semantic segmentation. In: CVPR, pp. 7151–7160 (2018)
Zhao, T., Wu, X.: Pyramid feature attention network for saliency detection. In: CVPR, pp. 3085–3094 (2019)
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Jiang, C., Zhang, Y., Wang, J., Chen, W. (2021). Approximated Masked Global Context Network for Skin Lesion Segmentation. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12893. Springer, Cham. https://doi.org/10.1007/978-3-030-86365-4_49
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