{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T22:50:17Z","timestamp":1754261417639,"version":"3.37.3"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"37-38","license":[{"start":{"date-parts":[[2020,7,22]],"date-time":"2020-07-22T00:00:00Z","timestamp":1595376000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,22]],"date-time":"2020-07-22T00:00:00Z","timestamp":1595376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61771056"],"award-info":[{"award-number":["61771056"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&D Program of China","award":["2017YFC0110700"],"award-info":[{"award-number":["2017YFC0110700"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1007\/s11042-020-09334-2","type":"journal-article","created":{"date-parts":[[2020,7,22]],"date-time":"2020-07-22T19:03:38Z","timestamp":1595444618000},"page":"27115-27136","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Attentive boundary aware network for multi-scale skin lesion segmentation with adversarial training"],"prefix":"10.1007","volume":"79","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8486-0942","authenticated-orcid":false,"given":"Zenghui","family":"Wei","sequence":"first","affiliation":[]},{"given":"Feng","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Hong","family":"Song","sequence":"additional","affiliation":[]},{"given":"Weixing","family":"Ji","sequence":"additional","affiliation":[]},{"given":"Guanghui","family":"Han","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,7,22]]},"reference":[{"key":"9334_CR1","doi-asserted-by":"crossref","unstructured":"Ahn E, Bi L, Jung Y, Kim J, Li C, Fulham MJ, Feng DD (2015) Automated saliency-based lesion segmentation in dermoscopic images. In: EMBC 2015, Milan, Italy, 25\u201329 Aug. 2015, pp 3009\u20133012","DOI":"10.1109\/EMBC.2015.7319025"},{"issue":"6","key":"9334_CR2","doi-asserted-by":"publisher","first-page":"1685","DOI":"10.1109\/JBHI.2017.2653179","volume":"21","author":"E Ahn","year":"2017","unstructured":"Ahn E, Kim J, Bi L, Kumar A, Li C, Fulham MJ, Feng DD (2017) Saliency-based lesion segmentation via background detection in dermoscopic images. IEEE J Biomed Health Inform 21(6):1685\u20131693","journal-title":"IEEE J Biomed Health Inform"},{"key":"9334_CR3","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.cmpb.2018.05.027","volume":"162","author":"MA Al-masni","year":"2018","unstructured":"Al-masni MA, Al-antari MA, Choi M-T, Han S-M, Kim T-S (2018) Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks. Comput Methods Prog Biomed 162:221\u2013231","journal-title":"Comput Methods Prog Biomed"},{"issue":"12","key":"9334_CR4","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for scene segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481\u20132495","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9334_CR5","unstructured":"Berseth M (2017) Isic 2017-skin lesion analysis towards melanoma detection. arXiv:1703.00523"},{"key":"9334_CR6","doi-asserted-by":"crossref","unstructured":"Bi L, Kim J, Ahn E, Feng D, Fulham M (2016) Automated skin lesion segmentation via image-wise supervised learning and multi-scale superpixel based cellular automata. In: ISBI 2016, Prague, Czech Republic, 13\u201316 April 2016, pp 1059\u20131062","DOI":"10.1109\/ISBI.2016.7493448"},{"key":"9334_CR7","unstructured":"Bi L, Jinman K, Ahn E, Feng D (2017) Automatic skin lesion analysis using large-scale dermoscopy images and deep residual networks. arXiv:1703.04197"},{"key":"9334_CR8","doi-asserted-by":"crossref","unstructured":"Bissoto A, Perez F, Valle E, Avila S (2018) Skin lesion synthesis with generative adversarial networks. arXiv:1902.03253","DOI":"10.1007\/978-3-030-01201-4_32"},{"issue":"3","key":"9334_CR9","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1111\/j.1600-0846.2008.00301.x","volume":"14","author":"ME Celebi","year":"2008","unstructured":"Celebi ME, Kingravi HA, Iyatomi H, Aslandogan YA, Stoecker WV, Moss RH, Malters JM, Grichnik JM, Marghoob AA, Rabinovitz HS, Menzies SW (2008) Border detection in dermoscopy images using statistical region merging. Skin Res Technol 14(3):347\u2013353","journal-title":"Skin Res Technol"},{"key":"9334_CR10","doi-asserted-by":"crossref","unstructured":"Chen L-C, Yang Y, Wang J, Xu W, Yuille AL (2016) Attention to scale: Scale-aware semantic image segmentation. In: CVPR 2016, Las Vegas, NV, USA, 27\u201330 June, 2016, pp 3640\u20133649","DOI":"10.1109\/CVPR.2016.396"},{"key":"9334_CR11","unstructured":"Chen L-C, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587"},{"key":"9334_CR12","doi-asserted-by":"crossref","unstructured":"Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: ECCV 2018, 8\u201314 September 2018, Munich, Germany, pp.833\u2013851","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"9334_CR13","first-page":"168","volume":"2018","author":"NCF Codella","year":"2018","unstructured":"Codella NCF, Gutman D, Celebi ME, Helba B, Marchetti MA, Dusza SW, Kalloo A, Liopyris K, Mishra NK, Kittler H, Halpern A (2018) Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). ISBI 2018:168\u2013172","journal-title":"ISBI"},{"key":"9334_CR14","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.neucom.2018.12.045","volume":"333","author":"A Dakhia","year":"2019","unstructured":"Dakhia A, Wang T, Lu H (2019) Multi-scale pyramid pooling network for salient object detection. Neurocomputing 333:211\u2013220","journal-title":"Neurocomputing"},{"key":"9334_CR15","first-page":"248","volume":"2009","author":"J Deng","year":"2009","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database, in Proc. IEEE Conf Comput Vis Pattern Recognit 2009:248\u2013255","journal-title":"IEEE Conf Comput Vis Pattern Recognit"},{"issue":"8","key":"9334_CR16","doi-asserted-by":"publisher","first-page":"1915","DOI":"10.1109\/TPAMI.2012.231","volume":"35","author":"C Farabet","year":"2013","unstructured":"Farabet C, Couprie C, Najman L, LeCun Y (2013) Learning hierarchical features for scene labeling. IEEE Trans Pattern Anal Mach Intell 35(8):1915\u20131929","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9334_CR17","unstructured":"Gutman D, Codella NC, Celebi E, Helba B, Marchetti M, Mishra N, Halpern A (2016) Skin lesion analysis toward melanoma detection: a challenge at the international symposium on biomedical imaging (isbi) 2016\u2033, hosted by the international skin imaging collaboration (ISIC). arXiv:1605.01397"},{"key":"9334_CR18","doi-asserted-by":"crossref","unstructured":"Hariharan B, Arbel\u00e1ez P, Girshick R, Malik J (2015) Hypercolumns for object segmentation and fine-grained localization. In: CVPR 2015, 7\u201312 June 2015, Boston, USA, pp 447\u2013456","DOI":"10.1109\/CVPR.2015.7298642"},{"key":"9334_CR19","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep Residual Learning for Image Recognition,\" 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, pp. 770\u2013778, https:\/\/doi.org\/10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90"},{"key":"9334_CR20","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Albanie S, Sun G, Wu E (2018) Squeeze-and-Excitation Networks. In: CVPR 2018, Salt Lake City, USA, 19\u201321 June, 2018, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"9334_CR21","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","volume":"36","author":"K Kamnitsas","year":"2017","unstructured":"Kamnitsas K, Ledig C, Newcombe VFJ, Simpson JP, Kane AD, Menon DK, Rueckert D, Glocker B (2017) Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal 36:61\u201378","journal-title":"Med Image Anal"},{"key":"9334_CR22","doi-asserted-by":"crossref","unstructured":"Lin BS, Michael K, Kalra S, Tizhoosh HR (2017) Skin lesion segmentation: U-Nets versus clustering. In: SSCI 2017, November 2017, pp 1\u20137","DOI":"10.1109\/SSCI.2017.8280804"},{"key":"9334_CR23","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: CVPR 2015, 7\u201312 June 2015, Boston, USA, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"2","key":"9334_CR24","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1109\/JBHI.2015.2390032","volume":"20","author":"Z Ma","year":"2016","unstructured":"Ma Z, Tavares JMRS (2016) A novel approach to segment skin lesions in dermoscopic images based on a deformable model. IEEE J Biomed Health Inform 20(2):615\u2013623","journal-title":"IEEE J Biomed Health Inform"},{"key":"9334_CR25","doi-asserted-by":"crossref","unstructured":"Mendon\u00e7a T, Ferreira PM, Marques JS, Marcal AR, Rozeira J (2013) PH2-A dermoscopic image database for research and benchmarking. In: EMBC 2013, 3\u20137 July 2013, pp. 5437\u20135440","DOI":"10.1109\/EMBC.2013.6610779"},{"issue":"Suppl 6","key":"9334_CR26","doi-asserted-by":"publisher","first-page":"S23","DOI":"10.1186\/1471-2105-11-S6-S23","volume":"11","author":"M Mete","year":"2010","unstructured":"Mete M, Sirakov NM (2010) Lesion detection in demoscopy images with novel density-based and active contour approaches. BMC Bioinformatics 11(Suppl 6):S23","journal-title":"BMC Bioinformatics"},{"key":"9334_CR27","doi-asserted-by":"crossref","unstructured":"Noh H, Hong S, Han B (2015) Learning deconvolution network for semantic segmentation. In: ICCV 2015, 7\u201313 December 2015, Santiago, Chile, pp 1520\u20131528","DOI":"10.1109\/ICCV.2015.178"},{"key":"9334_CR28","unstructured":"Oktay O, Schlemper J, Le Folgoc L (2018) Attention U-Net: Learning where to look for the pancreas. In: MIDL 2018, 4\u20136 July, 2018, pp 1\u201310"},{"issue":"3","key":"9334_CR29","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1007\/s00521-016-2482-6","volume":"29","author":"RB Oliveira","year":"2018","unstructured":"Oliveira RB, Papa JP, Pereira AS, Tavares JMRS (2018) Computational methods for pigmented skin lesion classification in images: review and future trends. Neural Comput Applic 29(3):613\u2013636","journal-title":"Neural Comput Applic"},{"key":"9334_CR30","doi-asserted-by":"crossref","unstructured":"Rahman M, Alpaslan N, Bhattacharya P (2016) Developing a retrieval based diagnostic aid for automated melanoma recognition of dermoscopic images. In: IEEE Applied Imagery Pattern Recognition Workshop, Washington, DC, USA, 18\u201320 Oct. 2016, pp 1\u20137","DOI":"10.1109\/AIPR.2016.8010594"},{"issue":"10","key":"9334_CR31","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.1001\/jamadermatol.2015.1187","volume":"151","author":"HW Rogers","year":"2015","unstructured":"Rogers HW, Weinstock MA, Feldman SR, Coldiron BM (2015) Incidence estimate of nonmelanoma skin cancer (keratinocyte carcinomas) in the U.S. population, 2012. JAMA Dermatol 151(10):1081\u20131086","journal-title":"JAMA Dermatol"},{"key":"9334_CR32","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. In: MICCAI 2015, 5\u20139 October 2015, Munich, Germany, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"9334_CR33","unstructured":"Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X (2016) Improved techniques for training GANs. In: NIPS 2016, Barcelona, Spain, June 2016"},{"key":"9334_CR34","doi-asserted-by":"crossref","unstructured":"Sarker Md. MK, Rashwan HA, Akram F (2018) SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks. In: MICCAI 2018, Granada, Spain, 16\u201320 September 2018","DOI":"10.1007\/978-3-030-00934-2_3"},{"issue":"4","key":"9334_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):1\u20131","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9334_CR36","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21332","volume":"66","author":"RL Siegel","year":"2016","unstructured":"Siegel RL, Miller KD, Jemal A (2016) Cancer statistics, 2016. CA Cancer J Clin 66:7\u201330","journal-title":"CA Cancer J Clin"},{"issue":"4","key":"9334_CR37","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1002\/ijc.30708","volume":"141","author":"J Tang","year":"2017","unstructured":"Tang J, Hou X, Yang C, Li Y, Xin Y, Guo W, Wei Z, Liu Y, Jiang G (2017) Recent developments in nanomedicine for melanoma treatment. Int J Cancer 141(4):646\u2013653","journal-title":"Int J Cancer"},{"key":"9334_CR38","doi-asserted-by":"crossref","unstructured":"Wang F, Jiang M, Qian C, Yang S, Li C, Zhang H, Wang X, Tang X (2017) Residual Attention Network for Image Classification. In: CVPR 2017, 21\u201326 July, 2017, pp 6450\u20136458","DOI":"10.1109\/CVPR.2017.683"},{"key":"9334_CR39","doi-asserted-by":"crossref","unstructured":"Wang TC, Liu MY, Zhu JY, Tao A, Kautz J, Catanzaro B (2018) High resolution image synthesis and semantic manipulation with conditional GANs. In: CVPR 2018, Salt Lake City, 19-21 June, 2018, pp 8798-8807","DOI":"10.1109\/CVPR.2018.00917"},{"issue":"6","key":"9334_CR40","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1007\/s12021-018-9377-x","volume":"16","author":"Y Xue","year":"2018","unstructured":"Xue Y, Xu T, Han Z, Rodney Long L, Huang X (2018) SegAN: adversarial network with multi-scale L1 loss for medical image segmentation. Neuroinformatics 16(6):383\u2013392","journal-title":"Neuroinformatics"},{"issue":"4","key":"9334_CR41","doi-asserted-by":"publisher","first-page":"994","DOI":"10.1109\/TMI.2016.2642839","volume":"36","author":"L Yu","year":"2017","unstructured":"Yu L, Chen H, Dou Q, Qin J, Heng PA (2017) Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans Med Imaging 36(4):994\u20131004","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"9334_CR42","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1109\/JBHI.2017.2787487","volume":"23","author":"Y Yuan","year":"2019","unstructured":"Yuan Y, Lo Y-C (2019) Improving Dermoscopic image segmentation with enhanced convolutional-Deconvolutional networks. IEEE J Biomed Health Inform 23(2):519\u2013526","journal-title":"IEEE J Biomed Health Inform"},{"issue":"9","key":"9334_CR43","doi-asserted-by":"publisher","first-page":"1876","DOI":"10.1109\/TMI.2017.2695227","volume":"36","author":"Y Yuan","year":"2017","unstructured":"Yuan Y, Chao M, Lo Y-C (2017) Automatic skin lesion segmentation using deep fully convolutional networks with Jaccard distance. IEEE Trans Med Imaging 36(9):1876\u20131886","journal-title":"IEEE Trans Med Imaging"},{"issue":"4","key":"9334_CR44","doi-asserted-by":"publisher","first-page":"976","DOI":"10.1109\/TFUZZ.2009.2018300","volume":"17","author":"ME Y\u00fcksel","year":"2009","unstructured":"Y\u00fcksel ME, Borlu M (2009) Accurate segmentation of dermoscopic images by image thresholding based on type-2 fuzzy logic. IEEE Trans Fuzzy Syst 17(4):976\u2013982","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"9","key":"9334_CR45","doi-asserted-by":"publisher","first-page":"2092","DOI":"10.1109\/TMI.2019.2893944","volume":"38","author":"J Zhang","year":"2019","unstructured":"Zhang J, Xie Y, Xia Y, Shen C (2019) Attention residual learning for skin lesion classification. IEEE Trans Med Imaging 38(9):2092\u20132103","journal-title":"IEEE Trans Med Imaging"},{"key":"9334_CR46","doi-asserted-by":"crossref","unstructured":"Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: CVPR 2017, Honolulu, HI, USA, 21\u201326 July, 2017, pp 2881\u20132890","DOI":"10.1109\/CVPR.2017.660"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09334-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-09334-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09334-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T23:47:02Z","timestamp":1626911222000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-09334-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,22]]},"references-count":46,"journal-issue":{"issue":"37-38","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["9334"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-09334-2","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2020,7,22]]},"assertion":[{"value":"30 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 June 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 July 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}