{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:30:50Z","timestamp":1750221050576,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":22,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,11,2]],"date-time":"2018-11-02T00:00:00Z","timestamp":1541116800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2018,11,2]]},"DOI":"10.1145\/3290420.3290461","type":"proceedings-article","created":{"date-parts":[[2019,3,5]],"date-time":"2019-03-05T20:48:03Z","timestamp":1551818883000},"page":"86-90","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["3D anisotropie convolutional neural network with step transfer learning for liver segmentation"],"prefix":"10.1145","author":[{"given":"Xiaoying","family":"Pan","sequence":"first","affiliation":[{"name":"Xi'an University of Posts and Telecommunications, Xi'an"}]},{"given":"Zhe","family":"Zhang","sequence":"additional","affiliation":[{"name":"Xi'an University of Posts and Telecommunications, Xi'an"}]},{"given":"Yuping","family":"Zhang","sequence":"additional","affiliation":[{"name":"Jiao Tong University, Xi'an"}]}],"member":"320","published-online":{"date-parts":[[2018,11,2]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"World cancer report 2014{M}","author":"Stewart B","year":"2014","unstructured":"Stewart B , Wild C. World cancer report 2014{M} . International Agency for Research on Cancer, 2014 . Stewart B, Wild C. World cancer report 2014{M}. International Agency for Research on Cancer, 2014."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2009.2013851"},{"key":"e_1_3_2_1_3_1","volume-title":"Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning{J}","author":"Zhang J","year":"2017","unstructured":"F Zhang J , Xia Y , Wu Q , Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning{J} . 2017 . F Zhang J, Xia Y, Wu Q, et al. Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning{J}. 2017."},{"key":"e_1_3_2_1_4_1","volume-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation{C}\/\/ International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Ronneberger O","year":"2015","unstructured":"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 , Cham , 2015 : 234--241. 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, Cham, 2015:234--241."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.09.010"},{"key":"e_1_3_2_1_6_1","volume-title":"Automatic Liver Lesion Detection using Cascaded Deep Residual Networks{J}","author":"Bi L","year":"2017","unstructured":"Bi L , Kim J , Kumar A , Automatic Liver Lesion Detection using Cascaded Deep Residual Networks{J} . 2017 . Bi L, Kim J, Kumar A, et al. Automatic Liver Lesion Detection using Cascaded Deep Residual Networks{J}. 2017."},{"key":"e_1_3_2_1_7_1","volume-title":"Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks{J}","author":"Christ P F","year":"2017","unstructured":"Christ P F , Ettlinger F , Gr\u00fcn F , Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks{J} . 2017 . Christ P F, Ettlinger F, Gr\u00fcn F, et al. Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks{J}. 2017."},{"key":"e_1_3_2_1_8_1","volume-title":"Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method{J}","author":"Han X.","year":"2017","unstructured":"Han X. Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method{J} . 2017 . Han X. Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method{J}. 2017."},{"key":"e_1_3_2_1_9_1","unstructured":"He K Zhang X Ren S etal Deep Residual Learning for Image Recognition{J}. 2015:770--778.  He K Zhang X Ren S et al. Deep Residual Learning for Image Recognition{J}. 2015:770--778."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"e_1_3_2_1_11_1","volume-title":"Rethinking Atrous Convolution for Semantic Image Segmentation{J}","author":"Chen L C","year":"2017","unstructured":"Chen L C , Papandreou G , Schroff F , Rethinking Atrous Convolution for Semantic Image Segmentation{J} . 2017 . Chen L C, Papandreou G, Schroff F, et al. Rethinking Atrous Convolution for Semantic Image Segmentation{J}. 2017."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2572683"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0733-5"},{"issue":"4","key":"e_1_3_2_1_14_1","first-page":"357","article-title":"Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs{J}","volume":"2014","author":"Chen L C","unstructured":"Chen L C , Papandreou G , Kokkinos I , Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs{J} . Computer Science , 2014 ( 4 ): 357 -- 361 . Chen L C, Papandreou G, Kokkinos I, et al. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs{J}. Computer Science, 2014(4):357--361.","journal-title":"Computer Science"},{"key":"e_1_3_2_1_15_1","volume-title":"Multi-Scale Context Aggregation by Dilated Convolutions{J}","author":"Yu F","year":"2016","unstructured":"Yu F , Koltun V. Multi-Scale Context Aggregation by Dilated Convolutions{J} . 2016 . Yu F, Koltun V. Multi-Scale Context Aggregation by Dilated Convolutions{J}. 2016."},{"key":"e_1_3_2_1_16_1","volume-title":"Multi-Scale Convolutional Architecture for Semantic Segmentation{J}","author":"Raj A","year":"2015","unstructured":"Raj A , Maturana D , Scherer S. Multi-Scale Convolutional Architecture for Semantic Segmentation{J} . 2015 . Raj A, Maturana D, Scherer S. Multi-Scale Convolutional Architecture for Semantic Segmentation{J}. 2015."},{"key":"e_1_3_2_1_17_1","volume-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation{C}\/\/ International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Ronneberger O","year":"2015","unstructured":"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 , Cham , 2015 : 234--241. 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, Cham, 2015:234--241."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Christ P F Elshaer M E A Ettlinger F et al. Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields{J}. 2016:415--423.  Christ P F Elshaer M E A Ettlinger F et al. Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields{J}. 2016:415--423.","DOI":"10.1007\/978-3-319-46723-8_48"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.11.005"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"\u00d6zg\u00fcn \u00c7i\u00e7ek Abdulkadir A Lienkamp S S etal 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation{J}. 2016:424--432.  \u00d6zg\u00fcn \u00c7i\u00e7ek Abdulkadir A Lienkamp S S et al. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation{J}. 2016:424--432.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"e_1_3_2_1_21_1","first-page":"1","article-title":"H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes{J}","volume":"2017","author":"Li X","unstructured":"Li X , Chen H , Qi X , H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes{J} . IEEE Transactions on Medical Imaging , 2017 , PP(99): 1 -- 1 . Li X, Chen H, Qi X, et al. H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes{J}. IEEE Transactions on Medical Imaging, 2017, PP(99):1--1.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_2_1_22_1","volume-title":"Maxim Berman Amal Rannen Triki, and B. Blaschko. \"The Lov\u00e1sz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks","author":"Matthew","year":"2018","unstructured":"Matthew , Maxim Berman Amal Rannen Triki, and B. Blaschko. \"The Lov\u00e1sz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks .\" ( 2018 ). Matthew, Maxim Berman Amal Rannen Triki, and B. Blaschko. \"The Lov\u00e1sz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks.\" (2018)."}],"event":{"name":"ICCIP 2018: 2018 the 4th International Conference on Communication and Information Processing","acronym":"ICCIP 2018","location":"Qingdao China"},"container-title":["Proceedings of the 4th International Conference on Communication and Information Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3290420.3290461","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3290420.3290461","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:44:16Z","timestamp":1750207456000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3290420.3290461"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,2]]},"references-count":22,"alternative-id":["10.1145\/3290420.3290461","10.1145\/3290420"],"URL":"https:\/\/doi.org\/10.1145\/3290420.3290461","relation":{},"subject":[],"published":{"date-parts":[[2018,11,2]]},"assertion":[{"value":"2018-11-02","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}