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Chinese Journal of Medical Imaging Technology , 2009 , 25 ( 11 ): 2106 - 2109 . Li L Q, Li D P, Wang Z M, Clinical value of 18F -FDG PET\/CT in diagnosis and staging of malignant melanoma[J]. Chinese Journal of Medical Imaging Technology, 2009, 25(11): 2106-2109.","journal-title":"Chinese Journal of Medical Imaging Technology"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.micron.2018.01.010"},{"issue":"2","key":"e_1_3_2_1_3_1","first-page":"12","article-title":"Beyond pixels:A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation[J]","volume":"34","author":"Zhu H","year":"2015","unstructured":"Zhu H , Mng F , Cai J , Beyond pixels:A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation[J] . Journal of Visual Communication & Image Representation , 2015 , 34 ( 2 ): 12 - 27 . Zhu H,Mng F,Cai J,et al. 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A survey on applications of machine learning techniques for medical image segmentation[J] . International Journal of Engineering and Technology , 2018 , 7 ( 4 ): 4489 - 4495 . Jena M, Mishra S P, Mishra D. A survey on applications of machine learning techniques for medical image segmentation[J]. International Journal of Engineering and Technology, 2018, 7(4): 4489-4495.","journal-title":"International Journal of Engineering and Technology"},{"volume-title":"Joel J.P.C R, Automatic segmentation of melanoma skin cancer using deep learning[C]\/\/IEEE International Conference on E-Health Networking, Application and Services","year":"2021","author":"Rafael Luz A","key":"e_1_3_2_1_7_1","unstructured":"Rafael Luz A , Ricardo De Andrade L R , Joel J.P.C R, Automatic segmentation of melanoma skin cancer using deep learning[C]\/\/IEEE International Conference on E-Health Networking, Application and Services , Shenzhen : IEEE , 2021 . Rafael Luz A, Ricardo De Andrade L R, Joel J.P.C R, Automatic segmentation of melanoma skin cancer using deep learning[C]\/\/IEEE International Conference on E-Health Networking, Application and Services, Shenzhen: IEEE, 2021."},{"volume-title":"Kokkinos I","year":"2017","author":"Chen L C","key":"e_1_3_2_1_8_1","unstructured":"Chen L C , Papandreou G , Kokkinos I , Deeplab : Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs[J]. IEEE transactions on pattern analysis and machine intelligence, 2017 , 40(4): 834-848. Chen L C, Papandreou G, Kokkinos I, Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 40(4): 834-848."},{"volume-title":"Multi-class semantic segmentation of skin lesions via fully convolutional networks[C]\/\/11th International Conference on Bioinformatics Models, Methods and Algorithms","year":"2020","author":"Manu G","key":"e_1_3_2_1_9_1","unstructured":"Manu G , Moi Hoon Y , Saeed H. Multi-class semantic segmentation of skin lesions via fully convolutional networks[C]\/\/11th International Conference on Bioinformatics Models, Methods and Algorithms , Valletta : BIOSTEC , 2020 : 290-294. Manu G, Moi Hoon Y, Saeed H. Multi-class semantic segmentation of skin lesions via fully convolutional networks[C]\/\/11th International Conference on Bioinformatics Models, Methods and Algorithms, Valletta: BIOSTEC, 2020:290-294."},{"issue":"1","key":"e_1_3_2_1_10_1","first-page":"166","article-title":"A computed tomography image segmentation algorithm for improving the diagnostic accuracy of rectal cancer based on U-net and residual block[J]","volume":"39","author":"Wang H","year":"2022","unstructured":"Wang H , Ji B N , He G , A computed tomography image segmentation algorithm for improving the diagnostic accuracy of rectal cancer based on U-net and residual block[J] . Shengwu Yixue Gongchengxue Zazhi , 2022 , 39 ( 1 ): 166 - 174 . Wang H, Ji B N, He G, A computed tomography image segmentation algorithm for improving the diagnostic accuracy of rectal cancer based on U-net and residual block[J]. Shengwu Yixue Gongchengxue Zazhi, 2022, 39(1):166-174.","journal-title":"Shengwu Yixue Gongchengxue Zazhi"},{"issue":"3","key":"e_1_3_2_1_11_1","first-page":"401","volume":"44","author":"Tian J X","year":"2018","unstructured":"Tian J X , Liu G C , Gu S S , Deep Learning in Medical Image Analysis and Its Challenges[J] . ACTA AUTOMATICA SINICA , 2018 , 44 ( 3 ): 401 \u2212 424 . Tian J X, Liu G C, Gu S S, Deep Learning in Medical Image Analysis and Its Challenges[J]. ACTA AUTOMATICA SINICA, 2018, 44(3): 401\u2212424.","journal-title":"ACTA AUTOMATICA SINICA"},{"issue":"5","key":"e_1_3_2_1_12_1","first-page":"1427","volume":"32","author":"Song J","year":"2021","unstructured":"Song J , Xiao L , Lian Z C , Overview and Prospect of Deep Learning for Image Segmentation in Digital Pathology[J] . Jurnal of Software , 2021 , 32 ( 5 ): 1427 \u2212 1460 . Song J, Xiao L, Lian Z C, Overview and Prospect of Deep Learning for Image Segmentation in Digital Pathology[J]. Jurnal of Software, 2021, 32(5): 1427\u22121460.","journal-title":"Jurnal of Software"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3089661"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.37188\/OPE.20212904.0877"},{"volume-title":"Skin melanoma segmentation using recurrent and convolutional neural networks[C]\/\/ IEEE International Symposium on Biomedical Imaging","year":"2017","author":"Mohamed A","key":"e_1_3_2_1_15_1","unstructured":"Mohamed A , Mohamed H , Saeid N , Skin melanoma segmentation using recurrent and convolutional neural networks[C]\/\/ IEEE International Symposium on Biomedical Imaging , Melbourne : ISBI , 2017 , 292-296. Mohamed A, Mohamed H, Saeid N, Skin melanoma segmentation using recurrent and convolutional neural networks[C]\/\/ IEEE International Symposium on Biomedical Imaging, Melbourne: ISBI, 2017, 292-296."},{"key":"e_1_3_2_1_16_1","first-page":"197","article-title":"Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching[J]","volume":"2017","author":"Guo Y","unstructured":"Guo Y , Gao Y , Shen D . Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching[J] . Deep Learning for Medical Image Analysis , 2017 : 197 - 222 . Guo Y, Gao Y, Shen D. Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching[J]. Deep Learning for Medical Image Analysis, 2017:197-222.","journal-title":"Deep Learning for Medical Image Analysis"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2946078"},{"volume-title":"Nuclear Science Symposium and Medical Imaging Conference, Manchester: IEEE","year":"2019","author":"Fang Q","key":"e_1_3_2_1_18_1","unstructured":"Fang Q , Gu X , Yan J , A FCN-based Unsupervised Learning Model for Deformable Chest CT Image [C]\/\/ IEEE Nuclear Science Symposium and Medical Imaging Conference, Manchester: IEEE , 2019 . Fang Q, Gu X, Yan J, A FCN-based Unsupervised Learning Model for Deformable Chest CT Image[C]\/\/IEEE Nuclear Science Symposium and Medical Imaging Conference, Manchester: IEEE, 2019."},{"volume-title":"Brox T. U-net: Convolutional networks for biomedical image segmentation[C]\/\/ International Conference on Medical image computing and computer-assisted intervention","year":"2015","author":"Ronneberger O","key":"e_1_3_2_1_19_1","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 , Munich : MICCAI , 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, Munich: MICCAI, 2015: 234-241."},{"volume-title":"Tajbakhsh N, Unet++: A nested u-net architecture for medical image segmentation[C]\/\/International Workshop on Deep Learning in Medical Image Analysis","year":"2018","author":"Zhou Z","key":"e_1_3_2_1_20_1","unstructured":"Zhou Z , Siddiquee M M R , Tajbakhsh N, Unet++: A nested u-net architecture for medical image segmentation[C]\/\/International Workshop on Deep Learning in Medical Image Analysis , Granada : Springer , 2018 : 3-11. Zhou Z, Siddiquee M M R, Tajbakhsh N, Unet++: A nested u-net architecture for medical image segmentation[C]\/\/International Workshop on Deep Learning in Medical Image Analysis, Granada: Springer, 2018: 3-11."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.08.025"},{"volume-title":"International Conference on Computer Vision Workshop. Seoul: IEEE","author":"Reza A","key":"e_1_3_2_1_22_1","unstructured":"Reza A , Maryam Asadi-Aghbolaghi , Mahmood F , Bi-Directional Conv LSTM U-Net with Densley Connected Convolutions[C]\/\/IEEE\/ CVF International Conference on Computer Vision Workshop. Seoul: IEEE , 2019: 406-415. Reza A, Maryam Asadi-Aghbolaghi, Mahmood F, Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions[C]\/\/IEEE\/CVF International Conference on Computer Vision Workshop. Seoul: IEEE, 2019: 406-415."},{"volume-title":"Doubleu-net: A deep convolutional neural network for medical image segmentation[C]\/\/International Symposium on Computer-Based Medical Systems","year":"2020","author":"Jha D","key":"e_1_3_2_1_23_1","unstructured":"Jha D , Riegler M A , Johansen D , Doubleu-net: A deep convolutional neural network for medical image segmentation[C]\/\/International Symposium on Computer-Based Medical Systems , Online : IEEE , 2020 : 558-564. Jha D, Riegler M A, Johansen D, Doubleu-net: A deep convolutional neural network for medical image segmentation[C]\/\/International Symposium on Computer-Based Medical Systems, Online: IEEE, 2020: 558-564."},{"volume-title":"DC-Unet: rethinking the u-net architecture with dual channel efficient cnn for medical image segmentation[C]\/\/Medical Imaging 2021: Image Processing","year":"2021","author":"Lou S.","key":"e_1_3_2_1_24_1","unstructured":"Lou , S. Guan , M. H. Loew , DC-Unet: rethinking the u-net architecture with dual channel efficient cnn for medical image segmentation[C]\/\/Medical Imaging 2021: Image Processing , Online : SPIE , 2021 , 11596. Lou, S. Guan, M. H. Loew, DC-Unet: rethinking the u-net architecture with dual channel efficient cnn for medical image segmentation[C]\/\/Medical Imaging 2021: Image Processing, Online: SPIE, 2021, 11596."},{"volume-title":"ECA-Net: Effificient Channel Attention for Deep Convolutional Neural Networks[C]\/\/2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","year":"2020","author":"Qilong W","key":"e_1_3_2_1_25_1","unstructured":"Qilong W , Banggu W , Pengfei Z , ECA-Net: Effificient Channel Attention for Deep Convolutional Neural Networks[C]\/\/2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition , Online : IEEE , 2020 : 11531-11539. Qilong W, Banggu W, Pengfei Z, ECA-Net: Effificient Channel Attention for Deep Convolutional Neural Networks[C]\/\/2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Online: IEEE, 2020: 11531-11539."},{"key":"e_1_3_2_1_26_1","first-page":"1492","volume-title":"Online: IEEE","author":"Debesh J","year":"2021","unstructured":"Debesh J , Nikhil Kumar T , Sharib A , NanoNet : Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy[C]\/\/IEEE International Symposium on Computer-Based Medical Systems , Online: IEEE , 2021 , 1492 - 1500 . Debesh J, Nikhil Kumar T, Sharib A, NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy[C]\/\/IEEE International Symposium on Computer-Based Medical Systems, Online: IEEE, 2021, 1492-1500."},{"key":"e_1_3_2_1_27_1","first-page":"121","article-title":"Delving deep into spatial pooling for squeeze-and-excitation networks[J]","author":"Jin X","year":"2022","unstructured":"Jin X , Xie Yan P , Wei Xiu S , Delving deep into spatial pooling for squeeze-and-excitation networks[J] . Pattern Recognition , 2022 , 121 . Jin X, Xie YanP, Wei XiuS, Delving deep into spatial pooling for squeeze-and-excitation networks[J]. Pattern Recognition, 2022, 121.","journal-title":"Pattern Recognition"},{"volume-title":"Efficient inference in fully connected crfs with gaussian edge potentials[C]\/\/Advances in neural information processing systems","year":"2011","author":"Kr\u00e4henb\u00fchl P","key":"e_1_3_2_1_28_1","unstructured":"Kr\u00e4henb\u00fchl P , Koltun V. Efficient inference in fully connected crfs with gaussian edge potentials[C]\/\/Advances in neural information processing systems , Granada : NIPS , 2011 : 109-117. Kr\u00e4henb\u00fchl P, Koltun V. Efficient inference in fully connected crfs with gaussian edge potentials[C]\/\/Advances in neural information processing systems, Granada: NIPS, 2011: 109-117."},{"volume-title":"An Advanced Architecture for Medical Image Segmentation[C]\/\/IEEE International Symposium on Multimedia","year":"2019","author":"Debesh J","key":"e_1_3_2_1_29_1","unstructured":"Debesh J , Pia H S , Michael A R , Res UNet++ : An Advanced Architecture for Medical Image Segmentation[C]\/\/IEEE International Symposium on Multimedia , San Diego : ISM , 2019 : 225\u2013230. 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