{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T03:35:43Z","timestamp":1780457743875,"version":"3.54.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T00:00:00Z","timestamp":1635811200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T00:00:00Z","timestamp":1635811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2021,12]]},"DOI":"10.1007\/s10278-021-00526-2","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T20:39:24Z","timestamp":1635885564000},"page":"1387-1404","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Evolutionary Deep Attention Convolutional Neural Networks for 2D and 3D Medical Image Segmentation"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2611-8157","authenticated-orcid":false,"given":"Tahereh","family":"Hassanzadeh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daryl","family":"Essam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruhul","family":"Sarker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,11,2]]},"reference":[{"issue":"1","key":"526_CR1","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s10462-018-9633-3","volume":"52","author":"Q Abbas","year":"2019","unstructured":"Abbas Q, Ibrahim ME, Jaffar MA:\u00a0A comprehensive review of recent advances on deep vision systems.\u00a0Artif Intell Rev\u00a052(1):39\u201376, 2019","journal-title":"Artif Intell Rev"},{"key":"526_CR2","doi-asserted-by":"crossref","unstructured":"Back T: Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford university press,\u00a01996","DOI":"10.1093\/oso\/9780195099713.001.0001"},{"key":"526_CR3","unstructured":"Bahdanau D, Cho K, Bengio Y:\u00a0Neural machine translation by jointly learning to align and translate. arXiv preprint, 2014.\u00a0arXiv:14090473"},{"key":"526_CR4","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.neucom.2019.01.110","volume":"392","author":"M Baldeon-Calisto","year":"2020","unstructured":"Baldeon-Calisto M, Lai-Yuen SK:\u00a0Adaresu-net: Multiobjective adaptive convolutional neural network for medical image segmentation. Neurocomputing 392:325\u2013340,\u00a02020","journal-title":"Neurocomputing"},{"key":"526_CR5","unstructured":"Calisto MB, Lai-Yuen SK:\u00a0Adaen-net: An ensemble of adaptive 2d-3d fully convolutional networks for medical image segmentation. Neural Netw\u00a02020"},{"key":"526_CR6","doi-asserted-by":"crossref","unstructured":"Chen P, Sun Z, Bing L, Yang W:\u00a0Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 conference on empirical methods in natural language processing, 2017,\u00a0pp 452\u2013461","DOI":"10.18653\/v1\/D17-1047"},{"key":"526_CR7","doi-asserted-by":"crossref","unstructured":"Cheung B, Sable C: Hybrid evolution of convolutional networks. In 2011 10th International Conference on Machine Learning and Applications and Workshops, vol.\u00a01, IEEE, 2011,\u00a0pp.\u00a0293\u2013297","DOI":"10.1109\/ICMLA.2011.73"},{"key":"526_CR8","unstructured":"Chollet F, et\u00a0al: Keras. https:\/\/keras.io, 2015"},{"key":"526_CR9","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O: 3d u-net: learning dense volumetric segmentation from sparse annotation. In International conference on medical image computing and computer-assisted intervention, Springer, 2016,\u00a0pp.\u00a0424\u2013432","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"526_CR10","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.neunet.2012.02.023","volume":"32","author":"D Cire\u015fAn","year":"2012","unstructured":"Cire\u015fAn D, Meier U, Masci J, Schmidhuber J: Multi-column deep neural network for traffic sign classification. Neural netw. 32:333\u2013338,\u00a02012","journal-title":"Neural networks"},{"issue":"3","key":"526_CR11","doi-asserted-by":"publisher","first-page":"1767","DOI":"10.1007\/s10462-019-09719-2","volume":"53","author":"A Darwish","year":"2020","unstructured":"Darwish A, Hassanien AE, Das S: A survey of swarm and evolutionary computing approaches for deep learning.\u00a0Artif Intell Rev\u00a053, 3:1767\u20131812,\u00a02020","journal-title":"Artif Intell Rev"},{"issue":"3","key":"526_CR12","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2307\/1932409","volume":"26","author":"LR Dice","year":"1945","unstructured":"Dice LR: Measures of the amount of ecologic association between species. Ecology 26, 3:297\u2013302,\u00a01945","journal-title":"Ecology"},{"key":"526_CR13","doi-asserted-by":"crossref","unstructured":"Dong N, Xu M, Liang X, Jiang Y, Dai W, Xing E: Neural architecture search for adversarial medical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2019,\u00a0pp.\u00a0828\u2013836","DOI":"10.1007\/978-3-030-32226-7_92"},{"key":"526_CR14","doi-asserted-by":"crossref","unstructured":"Drozdzal M, Vorontsov E, Chartrand G, Kadoury S, Pal C: The importance of skip connections in biomedical image segmentation. In Deep Learning and Data Labeling for Medical Applications. Springer, 2016, pp.\u00a0179\u2013187","DOI":"10.1007\/978-3-319-46976-8_19"},{"key":"526_CR15","doi-asserted-by":"crossref","unstructured":"Fogel DB: Phenotypes, genotypes, and operators in evolutionary computation. In Proc. 1995 IEEE Int. Conf. Evolutionary Computation (ICEC 95), 1995,\u00a0pp.\u00a0193\u2013198","DOI":"10.1109\/ICEC.1995.489143"},{"key":"526_CR16","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H: Dual attention network for scene segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp.\u00a03146\u20133154","DOI":"10.1109\/CVPR.2019.00326"},{"key":"526_CR17","doi-asserted-by":"crossref","unstructured":"Fujino S, Mori N, Matsumoto K: Deep convolutional networks for human sketches by means of the evolutionary deep learning. In 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS), IEEE, 2017,\u00a0pp.\u00a01\u20135","DOI":"10.1109\/IFSA-SCIS.2017.8023302"},{"key":"526_CR18","doi-asserted-by":"crossref","unstructured":"Goldberg DE, Deb K: A comparative analysis of selection schemes used in genetic algorithms. In Foundations of genetic algorithms, vol.\u00a01. Elsevier, 1991, pp.\u00a069\u201393","DOI":"10.1016\/B978-0-08-050684-5.50008-2"},{"key":"526_CR19","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","volume":"187","author":"Y Guo","year":"2016","unstructured":"Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew MS: Deep learning for visual understanding: A review. Neurocomputing 187:27\u201348, 2016","journal-title":"Neurocomputing"},{"key":"526_CR20","doi-asserted-by":"crossref","unstructured":"Hassanzadeh T, Essam D, Sarker R: 2d to 3d evolutionary deep convolutional neural networks for medical image segmentation.\u00a0IEEE Trans Med Imaging,\u00a02020","DOI":"10.1007\/s10278-021-00526-2"},{"key":"526_CR21","doi-asserted-by":"crossref","unstructured":"Hassanzadeh T, Essam D, Sarker R: Evolutionary attention network for medical image segmentation. In 2020 The International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2020, pp.\u00a01\u20138","DOI":"10.1109\/DICTA51227.2020.9363425"},{"key":"526_CR22","doi-asserted-by":"crossref","unstructured":"Hassanzadeh T, Essam D, Sarker R: An evolutionary denseres deep convolutional neural network for medical image segmentation. IEEE Access,\u00a02020","DOI":"10.1109\/DICTA51227.2020.9363425"},{"key":"526_CR23","doi-asserted-by":"crossref","unstructured":"Hassanzadeh T, Essam D, Sarker R: Evou-net: an evolutionary deep fully convolutional neural network for medical image segmentation. In Proceedings of the 35th Annual ACM Symposium on Applied Computing,\u00a02020, pp.\u00a0181\u2013189","DOI":"10.1145\/3341105.3373856"},{"key":"526_CR24","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J: Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition 2016, pp.\u00a0770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"8","key":"526_CR25","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1109\/TMI.2009.2013851","volume":"28","author":"T Heimann","year":"2009","unstructured":"Heimann T, Van\u00a0Ginneken B, Styner MA, Arzhaeva Y, Aurich V, Bauer C, Beck A, Becker C, Beichel R, Bekes G, et\u00a0al: Comparison and evaluation of methods for liver segmentation from ct datasets.\u00a0IEEE Trans Med Imaging\u00a028, 8:1251\u20131265, 2009","journal-title":"IEEE transactions on medical imaging"},{"issue":"8","key":"526_CR26","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J: Long short-term memory.\u00a0Neural Comput\u00a09, 8:1735\u20131780, 1997","journal-title":"Neural Comput"},{"key":"526_CR27","doi-asserted-by":"crossref","unstructured":"Holland JH, et\u00a0al: Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, 1992","DOI":"10.7551\/mitpress\/1090.001.0001"},{"key":"526_CR28","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G: Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition,\u00a02018, pp.\u00a07132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"526_CR29","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ: Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition 2017, pp.\u00a04700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"issue":"8","key":"526_CR30","doi-asserted-by":"publisher","first-page":"5455","DOI":"10.1007\/s10462-020-09825-6","volume":"53","author":"A Khan","year":"2020","unstructured":"Khan A, Sohail A, Zahoora U, Qureshi AS: A survey of the recent architectures of deep convolutional neural networks.\u00a0Artif Intell Rev\u00a053, 8:5455\u20135516,\u00a02020","journal-title":"Artif Intell Rev"},{"issue":"3","key":"526_CR31","doi-asserted-by":"publisher","first-page":"404","DOI":"10.3390\/app9030404","volume":"9","author":"M Kola\u0159\u00edk","year":"2019","unstructured":"Kola\u0159\u00edk M, Burget R, Uher V, \u0158\u00edha K, Dutta MK: Optimized high resolution 3d dense-u-net network for brain and spine segmentation. Appl Sci 9, 3:404, 2019","journal-title":"Appl Sci"},{"issue":"6","key":"526_CR32","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE: Imagenet classification with deep convolutional neural networks.\u00a0Commun ACM\u00a060, 6:84\u201390, 2017","journal-title":"Commun ACM"},{"issue":"4","key":"526_CR33","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD: Backpropagation applied to handwritten zip code recognition.\u00a0Neural Comput\u00a01, 4:541\u2013551,\u00a01989","journal-title":"Neural Comput"},{"key":"526_CR34","unstructured":"Li H, Xiong P, An J, Wang L: Pyramid attention network for semantic segmentation. arXiv preprint,\u00a02018.\u00a0arXiv:1805.10180"},{"issue":"9","key":"526_CR35","first-page":"2508","volume":"36","author":"Y Li","year":"2016","unstructured":"Li Y, Hao Z, Lei H: Survey of convolutional neural network. J Comput Appl 36, 9:2508\u20132515, 2016","journal-title":"J Comput Appl"},{"key":"526_CR36","doi-asserted-by":"publisher","first-page":"104785","DOI":"10.1016\/j.knosys.2019.05.028","volume":"181","author":"Y Li","year":"2019","unstructured":"Li Y, Zhu Z, Kong D, Han H, Zhao Y: Ea-lstm: Evolutionary attention-based lstm for time series prediction. Knowledge-Based Systems 181:104785,\u00a02019","journal-title":"Knowledge-Based Systems"},{"issue":"2","key":"526_CR37","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1007\/s10462-018-9641-3","volume":"52","author":"X Liu","year":"2019","unstructured":"Liu X, Deng Z, Yang Y: Recent progress in semantic image segmentation. Artif Intell Rev 52, 2:1089\u20131106, 2019","journal-title":"Artif Intell Rev"},{"key":"526_CR38","doi-asserted-by":"crossref","unstructured":"Mane D, Kulkarni UV: A survey on supervised convolutional neural network and its major applications. In Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications. IGI Global, 2020, pp.\u00a01058\u20131071","DOI":"10.4018\/978-1-7998-0414-7.ch059"},{"key":"526_CR39","doi-asserted-by":"crossref","unstructured":"Mortazi A, Bagci U: Automatically designing cnn architectures for medical image segmentation. In International Workshop on Machine Learning in Medical Imaging, Springer, 2018,\u00a0pp.\u00a098\u2013106","DOI":"10.1007\/978-3-030-00919-9_12"},{"key":"526_CR40","doi-asserted-by":"crossref","unstructured":"Qin Z, Yu F, Liu C, Chen X: How convolutional neural network see the world-a survey of convolutional neural network visualization methods. arXiv preprint,\u00a02018.\u00a0arXiv:1804.11191","DOI":"10.3934\/mfc.2018008"},{"key":"526_CR41","doi-asserted-by":"publisher","first-page":"4780","DOI":"10.1609\/aaai.v33i01.33014780","volume":"33","author":"E Real","year":"2019","unstructured":"Real E, Aggarwal A, Huang Y, Le QV: Regularized evolution for image classifier architecture search. In Proceedings of the aaai conference on artificial intelligence,\u00a02019, vol.\u00a033, pp.\u00a04780\u20134789","journal-title":"In Proceedings of the aaai conference on artificial intelligence"},{"key":"526_CR42","unstructured":"Real E, Moore S, Selle A, Saxena S, Suematsu YL, Tan J, Le Q, Kurakin A: Large-scale evolution of image classifiers. arXiv preprint,\u00a02017.\u00a0arXiv:1703.01041"},{"key":"526_CR43","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T: U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, Springer, 2015,\u00a0pp.\u00a0234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"526_CR44","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","volume":"53","author":"J Schlemper","year":"2019","unstructured":"Schlemper J, Oktay O, Schaap M, Heinrich M, Kainz B, Glocker B, Rueckert D: Attention gated networks: Learning to leverage salient regions in medical images.\u00a0Med Image Anal\u00a053, 197\u2013207, 2019","journal-title":"Med Image Anal"},{"key":"526_CR45","doi-asserted-by":"crossref","unstructured":"Shen T, Zhou T, Long G, Jiang J, Pan S, Zhang C: Disan: Directional self-attention network for rnn\/cnn-free language understanding. In Proceedings of the AAAI Conference on Artificial Intelligence,\u00a02018, vol.\u00a032","DOI":"10.1609\/aaai.v32i1.11941"},{"key":"526_CR46","unstructured":"Simpson AL, Antonelli M, Bakas S, Bilello M, Farahani K, Van\u00a0Ginneken B, Kopp-Schneider A, Landman BA, Litjens G, Menze B, et\u00a0al: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint,\u00a02019.\u00a0arXiv:1902.09063"},{"issue":"2","key":"526_CR47","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1162\/106365602320169811","volume":"10","author":"KO Stanley","year":"2002","unstructured":"Stanley KO, Miikkulainen R: Evolving neural networks through augmenting topologies. Evol Comput 10, 2:99\u2013127, 2002","journal-title":"Evol Comput"},{"key":"526_CR48","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.neucom.2020.07.078","volume":"417","author":"Y Tian","year":"2020","unstructured":"Tian Y, Zhang Y, Zhou D, Cheng G, Chen WG, Wang R: Triple attention network for video segmentation. Neurocomputing 417:202\u2013211, 2020","journal-title":"Neurocomputing"},{"key":"526_CR49","doi-asserted-by":"crossref","unstructured":"Wang F, Jiang M, Qian C, Yang S, Li C, Zhang H, Wang X, Tang X: Residual attention network for image classification. In Proceedings of the IEEE conference on computer vision and pattern recognition,\u00a02017, pp.\u00a03156\u20133164","DOI":"10.1109\/CVPR.2017.683"},{"key":"526_CR50","doi-asserted-by":"publisher","first-page":"44247","DOI":"10.1109\/ACCESS.2019.2908991","volume":"7","author":"Y Weng","year":"2019","unstructured":"Weng Y, Zhou T, Li Y, Qiu X: Nas-unet: Neural architecture search for medical image segmentation. IEEE Access 7:44247\u201344257, 2019","journal-title":"IEEE Access"},{"key":"526_CR51","unstructured":"Yu F, Koltun V: Multi-scale context aggregation by dilated convolutions. arXiv preprint,\u00a02015.\u00a0arXiv:1511.07122"},{"key":"526_CR52","doi-asserted-by":"crossref","unstructured":"Yu L, Yang X, Chen H, Qin J, Heng PA: Volumetric convnets with mixed residual connections for automated prostate segmentation from 3d mr images. In Thirty-first AAAI conference on artificial intelligence,\u00a02017","DOI":"10.1609\/aaai.v31i1.10510"},{"key":"526_CR53","doi-asserted-by":"crossref","unstructured":"Zhang H, Jin Y, Cheng R, Hao K: Efficient evolutionary search of attention convolutional networks via sampled training and node inheritance.\u00a0IEEE Trans Evol Comput,\u00a02020","DOI":"10.1109\/TEVC.2020.3040272"},{"key":"526_CR54","unstructured":"Zoph B, Le QV: Neural architecture search with reinforcement learning. arXiv preprint,\u00a02016.\u00a0arXiv:1611.01578"},{"key":"526_CR55","doi-asserted-by":"crossref","unstructured":"Zoph B, Vasudevan V, Shlens J, Le QV: Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition,\u00a02018, pp.\u00a08697\u20138710","DOI":"10.1109\/CVPR.2018.00907"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-021-00526-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-021-00526-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-021-00526-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T09:37:10Z","timestamp":1726047430000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-021-00526-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,2]]},"references-count":55,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["526"],"URL":"https:\/\/doi.org\/10.1007\/s10278-021-00526-2","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,2]]},"assertion":[{"value":"8 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 October 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 November 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}