{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T15:18:08Z","timestamp":1772896688877,"version":"3.50.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,2,14]],"date-time":"2021-02-14T00:00:00Z","timestamp":1613260800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,2,14]],"date-time":"2021-02-14T00:00:00Z","timestamp":1613260800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1007\/s12559-020-09805-6","type":"journal-article","created":{"date-parts":[[2021,2,18]],"date-time":"2021-02-18T00:18:22Z","timestamp":1613607502000},"page":"583-594","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Dense Encoder-Decoder\u2013Based Architecture for Skin Lesion Segmentation"],"prefix":"10.1007","volume":"13","author":[{"given":"Saqib","family":"Qamar","sequence":"first","affiliation":[]},{"given":"Parvez","family":"Ahmad","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1420-0815","authenticated-orcid":false,"given":"Linlin","family":"Shen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,14]]},"reference":[{"key":"9805_CR1","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.compmedimag.2016.05.002","volume":"52","author":"A Pennisi","year":"2016","unstructured":"Pennisi A, Bloisi D, Nardi D, Giampetruzzi A, Mondino C, Facchiano A. Skin lesion image segmentation using delaunay triangulation for melanoma detection. Computerized Medical Imaging and Graphics 2016;52:89\u2013103.","journal-title":"Computerized Medical Imaging and Graphics"},{"issue":"4","key":"9805_CR2","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1109\/TBME.2018.2866166","volume":"66","author":"Z Yu","year":"2019","unstructured":"Yu Z, Jiang X, Zhou F, Qin J, Ni D, Chen S, et al. Melanoma recognition in dermoscopy images via aggregated deep convolutional features. IEEE Transactions on Biomedical Engineering 2019; 66(4):1006\u20131016.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"4","key":"9805_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3329784","volume":"52","author":"S Ghosh","year":"2019","unstructured":"Ghosh S, Das N, Das I, Maulik U. Understanding deep learning techniques for image segmentation. ACM Computing Survey 2019;52(4):1\u201330. Article No.: 73.","journal-title":"ACM Computing Survey"},{"key":"9805_CR4","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.media.2016.07.007","volume":"35","author":"T Kooi","year":"2017","unstructured":"Kooi T, Litjens G, Van Ginneken B, Gubern-M\u00e9rida A, S\u00e1nchez CI, Mann R, et al. Large scale deep learning for computer aided detection of mammographic lesions. Medical Image Analysis 2017;35:303\u2013312.","journal-title":"Medical Image Analysis"},{"key":"9805_CR5","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/s12559-019-09645-z","volume":"11","author":"B Hou","year":"2019","unstructured":"Hou B, Kang G, Zhang N, Liu K. Multi-target interactive neural network for automated segmentation of the hippocampus in magnetic resonance imaging. Cognitive Computation 2019;11:630\u2013643.","journal-title":"Cognitive Computation"},{"issue":"6","key":"9805_CR6","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1007\/s11548-017-1567-8","volume":"12","author":"MH Jafari","year":"2017","unstructured":"Jafari MH, Nasr-Esfahani E, Karimi N, Soroushmehr SMR, Samavi S, Najarian K. Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma. International Journal of Computer Assisted Radiology and Surgery 2017;12(6):1021\u20131030.","journal-title":"International Journal of Computer Assisted Radiology and Surgery"},{"issue":"9","key":"9805_CR7","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 YC. Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance. IEEE Trans Medical Imag 2017;36(9):1876\u20131886.","journal-title":"IEEE Trans Medical Imag"},{"issue":"2","key":"9805_CR8","doi-asserted-by":"publisher","first-page":"556","DOI":"10.3390\/s18020556","volume":"18","author":"Y Li","year":"2018","unstructured":"Li Y, Shen L. Skin lesion analysis towards melanoma detection using deep learning network. Sensors 2018;18(2):556.","journal-title":"Sensors"},{"key":"9805_CR9","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. Medical image computing and computer-assisted intervention (MICCAI). Cham: Springer International Publishing; 2015. p. 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"9805_CR10","doi-asserted-by":"crossref","unstructured":"Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J. UNet++: a nested u-net architecture for medical image segmentation. Deep learning in medical image analysis and multimodal learning for clinical decision support. Cham: Springer International Publishing; 2018. p. 3\u201311.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"9805_CR11","doi-asserted-by":"crossref","unstructured":"Lin BS, Michael K, Kalra S, Tizhoosh HR. Skin lesion segmentation: U-nets versus clustering. IEEE symposium series on computational intelligence (SSCI). IEEE; 2017. p. 1\u20137.","DOI":"10.1109\/SSCI.2017.8280804"},{"issue":"18","key":"9805_CR12","doi-asserted-by":"publisher","first-page":"25807","DOI":"10.1007\/s11042-019-07829-1","volume":"78","author":"S Qamar","year":"2019","unstructured":"Qamar S, Jin H, Zheng R, Ahmad P. Multi stream 3D hyper-densely connected network for multi modality isointense infant brain mri segmentation. Multimedia Tools and Applications 2019;78(18): 25807\u201325828.","journal-title":"Multimedia Tools and Applications"},{"key":"9805_CR13","doi-asserted-by":"publisher","first-page":"53942","DOI":"10.1109\/ACCESS.2020.2980996","volume":"8","author":"JJM Ople","year":"2020","unstructured":"Ople JJM, yi Yeh P, Sun SW, Tsai IT, Hua KL. Multi-scale neural network with dilated convolutions for image deblurring. IEEE Access 2020;8:53942\u201353952.","journal-title":"IEEE Access"},{"key":"9805_CR14","doi-asserted-by":"crossref","unstructured":"Holschneider M, Kronland-Martinet R, Morlet J, Tchamitchian P. A real-time algorithm for signal analysis with the help of the wavelet transform. Wavelets. In: Combes JM, Grossmann A, and Tchamitchian P, editors. Berlin: Springer; 1990. p. 286\u2013297.","DOI":"10.1007\/978-3-642-75988-8_28"},{"key":"9805_CR15","doi-asserted-by":"crossref","unstructured":"Yu F, Koltun V, Funkhouser T. Dilated residual networks. IEEE conference on computer vision and pattern recognition (CVPR); 2017. p. 636\u2013644.","DOI":"10.1109\/CVPR.2017.75"},{"key":"9805_CR16","unstructured":"Chen LC, Papandreou G, Schroff F, Adam H. 2017. Rethinking atrous convolution for semantic image segmentation. Available from: arXiv:1706.05587."},{"key":"9805_CR17","doi-asserted-by":"crossref","unstructured":"Jiang F, Zhou F, Qin J, Wang T, Lei B. Decision-augmented generative adversarial network for skin lesion segmentation. IEEE 16th international symposium on biomedical imaging (ISBI 2019); 2019. p. 447\u2013450.","DOI":"10.1109\/ISBI.2019.8759434"},{"key":"9805_CR18","doi-asserted-by":"crossref","unstructured":"Wang P, Chen P, Yuan Y, Liu D, Huang Z, Hou X, et al. Understanding convolution for semantic segmentation. IEEE winter conference on applications of computer vision (WACV). IEEE; 2018. p. 1451\u20131460.","DOI":"10.1109\/WACV.2018.00163"},{"key":"9805_CR19","doi-asserted-by":"crossref","unstructured":"Bi L, Feng D, Kim J. 2018. Improving automatic skin lesion segmentation using adversarial learning based data augmentation. Available from: arXiv:1807.08392.","DOI":"10.1109\/ISBI.2019.8759479"},{"issue":"1","key":"9805_CR20","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1109\/TMI.2016.2606380","volume":"36","author":"Y Song","year":"2016","unstructured":"Song Y, Tan EL, Jiang X, Cheng JZ, Ni D, Chen S, et al. Accurate cervical cell segmentation from overlapping clumps in pap smear images. IEEE Trans Medical Imag 2016;36(1):288\u2013300.","journal-title":"IEEE Trans Medical Imag"},{"key":"9805_CR21","doi-asserted-by":"crossref","unstructured":"Liao S, Gao Y, Oto A, Shen D. Representation learning: a unified deep learning framework for automatic prostate MR segmentation. International conference on medical image computing and computer-assisted intervention (MICCAI). Springer; 2013. p. 254\u2013261.","DOI":"10.1007\/978-3-642-40763-5_32"},{"issue":"4","key":"9805_CR22","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 2017;39(4):640\u2013651. Available from: https:\/\/doi.org\/10.1109\/TPAMI.2016.2572683.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9805_CR23","doi-asserted-by":"crossref","unstructured":"Zheng S, Jayasumana S, Romera-Paredes B, Vineet V, Su Z, Du D, et al. Conditional random fields as recurrent neural networks. IEEE international conference on computer vision (ICCV); 2015. p. 1529\u20131537.","DOI":"10.1109\/ICCV.2015.179"},{"key":"9805_CR24","doi-asserted-by":"crossref","unstructured":"Papandreou G, Chen LC, Murphy KP, Yuille AL. Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation. IEEE international conference on computer vision (ICCV); 2015. p. 1742\u20131750.","DOI":"10.1109\/ICCV.2015.203"},{"issue":"9","key":"9805_CR25","doi-asserted-by":"publisher","first-page":"4376","DOI":"10.1109\/TIP.2019.2910667","volume":"28","author":"Q Wang","year":"2019","unstructured":"Wang Q, Gao J, Li X. Weakly supervised adversarial domain adaptation for semantic segmentation in urban scenes. IEEE Transactions on Image Processing 2019;28(9):4376\u20134386.","journal-title":"IEEE Transactions on Image Processing"},{"issue":"5","key":"9805_CR26","doi-asserted-by":"publisher","first-page":"1457","DOI":"10.1109\/TITS.2017.2726546","volume":"19","author":"Q Wang","year":"2017","unstructured":"Wang Q, Gao J, Yuan Y. A joint convolutional neural networks and context transfer for street scenes labeling. IEEE Transactions on Intelligent Transportation Systems 2017;19(5):1457\u20131470.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"4","key":"9805_CR27","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL. Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence 2017;40(4):834\u2013848.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"9805_CR28","doi-asserted-by":"crossref","unstructured":"Zhang K, Zuo W, Gu S, Zhang L. Learning deep CNN denoiser prior for image restoration. IEEE conference on computer vision and pattern recognition (CVPR); 2017. p. 3929\u20133938.","DOI":"10.1109\/CVPR.2017.300"},{"key":"9805_CR29","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1109\/JBHI.2017.2787487","volume":"23","author":"Y Yuan","year":"2019","unstructured":"Yuan Y, Lo YC. Improving dermoscopic image segmentation with enhanced Convolutional-Deconvolutional networks. IEEE Journal of Biomedical and Health Informatics 2019;23:519\u2013526.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"9805_CR30","doi-asserted-by":"crossref","unstructured":"Kaul C, Manandhar S, Pears N. Focusnet: an attention-based fully convolutional network for medical image segmentation. IEEE 16th international symposium on biomedical imaging (ISBI 2019); 2019. p. 455\u2013458.","DOI":"10.1109\/ISBI.2019.8759477"},{"key":"9805_CR31","unstructured":"Bissoto A, Perez F, Ribeiro V, Fornaciali M, Avila S, Valle E. 2018. Deep-learning ensembles for skin-lesion segmentation, analysis, classification: RECOD Titans at ISIC Challenge 2018. arXiv:1808.08480."},{"key":"9805_CR32","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1109\/ISBI.2019.8759172","volume":"2019","author":"AH Shahin","year":"2019","unstructured":"Shahin AH, Amer K, Alattar MA. Deep convolutional encoder-decoders with aggregated multi-resolution skip connections for skin lesion segmentation. IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019;2019:451\u2013454.","journal-title":"IEEE 16th International Symposium on Biomedical Imaging (ISBI"},{"key":"9805_CR33","doi-asserted-by":"crossref","unstructured":"Wu J, Chen EZ, Rong R, Li X, Xu D, Jiang H. Skin lesion segmentation with C-UNet. 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC); 2019. p. 2785\u20132788.","DOI":"10.1109\/EMBC.2019.8857773"},{"key":"9805_CR34","doi-asserted-by":"crossref","unstructured":"Chen S, Wang Z, Shi J, Liu B, Yu N. A multi-task framework with feature passing module for skin lesion classification and segmentation. IEEE 15th international symposium on biomedical imaging (ISBI 2018); 2018. p. 1126\u20131129.","DOI":"10.1109\/ISBI.2018.8363769"}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-020-09805-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12559-020-09805-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-020-09805-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,9]],"date-time":"2021-03-09T13:37:45Z","timestamp":1615297065000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12559-020-09805-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,14]]},"references-count":34,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["9805"],"URL":"https:\/\/doi.org\/10.1007\/s12559-020-09805-6","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"value":"1866-9956","type":"print"},{"value":"1866-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,14]]},"assertion":[{"value":"25 June 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}