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Fully convolutional networks for semantic segmentation[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 3431-3440.  Long J Shelhamer E Darrell T. Fully convolutional networks for semantic segmentation[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 3431-3440.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_3_2_1_5_1","volume-title":"Fully convolutional network for liver segmentation and lesions detection[M]\/\/Deep learning and data labeling for medical applications","author":"Ben-Cohen A","year":"2016","unstructured":"Ben-Cohen A , Diamant I , Klang E , Fully convolutional network for liver segmentation and lesions detection[M]\/\/Deep learning and data labeling for medical applications . Springer , Cham , 2016 : 77-85. Ben-Cohen A, Diamant I, Klang E, Fully convolutional network for liver segmentation and lesions detection[M]\/\/Deep learning and data labeling for medical applications. 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Squeeze-and-excitation networks[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 7132-7141.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_3_2_1_21_1","volume-title":"Channel-Unet: a spatial channel-wise convolutional neural network for liver and tumors segmentation[J]. Frontiers in genetics","author":"Chen Y","year":"2019","unstructured":"Chen Y , Wang K , Liao X , Channel-Unet: a spatial channel-wise convolutional neural network for liver and tumors segmentation[J]. Frontiers in genetics , 2019 , 10: 1110. Chen Y, Wang K, Liao X, Channel-Unet: a spatial channel-wise convolutional neural network for liver and tumors segmentation[J]. 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U-Net: Convolutional Networks for Biomedical Image Segmentation[C]\/\/ International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer International Publishing , 2015 . Ronneberger O , Fischer P , Brox T . U-Net: Convolutional Networks for Biomedical Image Segmentation[C]\/\/ International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer International Publishing, 2015."},{"key":"e_1_3_2_1_26_1","volume-title":"A review on deep learning techniques applied to semantic segmentation[J]. arXiv preprint arXiv:1704.06857","author":"Garcia-Garcia A","year":"2017","unstructured":"Garcia-Garcia A , Orts-Escolano S , Oprea S , A review on deep learning techniques applied to semantic segmentation[J]. arXiv preprint arXiv:1704.06857 , 2017 . 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