{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T21:36:56Z","timestamp":1771623416381,"version":"3.50.1"},"reference-count":106,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2022,1,16]],"date-time":"2022-01-16T00:00:00Z","timestamp":1642291200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,16]],"date-time":"2022-01-16T00:00:00Z","timestamp":1642291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003399","name":"Shanghai Science and Technology Committee","doi-asserted-by":"crossref","award":["18411952100"],"award-info":[{"award-number":["18411952100"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003399","name":"Shanghai Science and Technology Committee","doi-asserted-by":"crossref","award":["17411953500"],"award-info":[{"award-number":["17411953500"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["62072358"],"award-info":[{"award-number":["62072358"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"national key r&d program of china under grant","award":["2020YFF0304900"],"award-info":[{"award-number":["2020YFF0304900"]}]},{"name":"national key r&d program of china under grant","award":["2019YFB1311600"],"award-info":[{"award-number":["2019YFB1311600"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s00521-021-06750-9","type":"journal-article","created":{"date-parts":[[2022,1,16]],"date-time":"2022-01-16T00:03:06Z","timestamp":1642291386000},"page":"6547-6567","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["MEDAS: an open-source platform as a service to help break the walls between medicine and informatics"],"prefix":"10.1007","volume":"34","author":[{"given":"Liang","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Johann","family":"Li","sequence":"additional","affiliation":[]},{"given":"Ping","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiaoyuan","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Maoguo","family":"Gong","sequence":"additional","affiliation":[]},{"given":"Peiyi","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Guangming","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Syed Afaq","family":"Shah","sequence":"additional","affiliation":[]},{"given":"Mohammed","family":"Bennamoun","sequence":"additional","affiliation":[]},{"given":"Kun","family":"Qian","sequence":"additional","affiliation":[]},{"given":"Bj\u00f6rn W.","family":"Schuller","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,16]]},"reference":[{"key":"6750_CR1","unstructured":"Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray DG, Steiner B, Tucker P, Vasudevan V, Warden P, Wicke M, Yu Y, Zheng X (2016) TensorFlow: a system for large-scale machine learning. In: Proceedings of the 12th USENIX symposium on operating systems design and implementation, OSDI 2016, vol abs\/1605.0, pp 265\u2013283 (2016). http:\/\/arxiv.org\/abs\/1605.08695"},{"key":"6750_CR2","doi-asserted-by":"publisher","unstructured":"Andrew AM (1999) The handbook of brain theory and neural. Networks. https:\/\/doi.org\/10.1108\/k.1999.28.9.1084.1. https:\/\/dl.acm.org\/citation.cfm?id=303568.303704","DOI":"10.1108\/k.1999.28.9.1084.1"},{"issue":"2","key":"6750_CR3","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","volume":"38","author":"SG Armato","year":"2011","unstructured":"Armato SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Van Beek EJ, Yankelevitz D, Biancardi AM, Bland PH, Brown MS, Engelmann RM, Laderach GE, Max D, Pais RC, Qing DP, Roberts RY, Smith AR, Starkey A, Batra P, Caligiuri P, Farooqi A, Gladish GW, Jude CM, Munden RF, Petkovska I, Quint LE, Schwartz LH, Sundaram B, Dodd LE, Fenimore C, Gur D, Petrick N, Freymann J, Kirby J, Hughes B, Vande Casteele A, Gupte S, Sallam M, Heath MD, Kuhn MH, Dharaiya E, Burns R, Fryd DS, Salganicoff M, Anand V, Shreter U, Vastagh S, Croft BY, Clarke LP (2011) The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys 38(2):915\u2013931. https:\/\/doi.org\/10.1118\/1.3528204","journal-title":"Med Phys"},{"key":"6750_CR4","doi-asserted-by":"publisher","unstructured":"Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC (2011) A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage 54(3):2033\u20132044.https:\/\/doi.org\/10.1016\/j.neuroimage.2010.09.025. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1053811910012061","DOI":"10.1016\/j.neuroimage.2010.09.025"},{"issue":"June","key":"6750_CR5","first-page":"2321","volume":"III","author":"R Beaulah Jeyavathana","year":"2016","unstructured":"Beaulah Jeyavathana R, Balasubramanian R, Pandian AA (2016) A survey: analysis on pre-processing and segmentation techniques for medical images. Int J Res Sci Innov III(June):2321\u20132705","journal-title":"Int J Res Sci Innov"},{"key":"6750_CR6","doi-asserted-by":"publisher","unstructured":"Beers A, Brown J, Chang K, Hoebel K, Patel J, Ly KI, Tolaney SM, Brastianos P, Rosen B, Gerstner ER, Kalpathy-Cramer J (2021) DeepNeuro: an open-source deep learning toolbox for neuroimaging. Neuroinformatics 19(1):127\u2013140.https:\/\/doi.org\/10.1007\/s12021-020-09477-5. https:\/\/arxiv.org\/abs\/1808.04589","DOI":"10.1007\/s12021-020-09477-5"},{"key":"6750_CR7","unstructured":"Bilic1a P, Christa PF, Vorontsov E, Chlebusr G, Chenm H, Doum Q, Fum CW, Hanp X, Hengm PA, Hesserq J, Kadourye S, Kopczyskiv T, Leo M, Lio C, Lim X, Lipkova J, Lowengrubn J, Meiner H, Moltzr JH, Pale C, Pirauda M, Qim X, Qil J, Rempera M, Rothq K, Schenkr A, Sekuboyinaa A, Zhouk P, Hulsemeyera C, Beetza M, Ettlingera F, Gruena F, Kaissisb G, Lohferb F, Brarenb R, Holchc J, Hofmannc F, Sommerc W, Heinemannc V, Jacobsd C, Mamanid GEH, Ginnekend BV, Chartrande G, Tange A, Drozdzale M, Kadourye S, Ben-Cohenf A, Klangf E, Amitaif MM, Konenf E, Greenspanf H, Moreaug J, Hostettlerg A, Solerg L, Vivantih R, Szeskinh A, Lev-Cohainh N, Sosnah J, Joskowiczh L, Kumarw A, Korex A, Wangy C, Fengz D, Liaa F, Krishnamurthix G, Heab J, Wuaa J, Kimx J, Zhouac J, Maad J, Liaa J, Maninisae KK, Kaluvax KC, Bix L, Khenedx M, Beliverae M, Linaa Q, Yangad X, Yuanaf Y, Chenaa Y, Liad Y, Qius Y, Wuad Y, Menzea B (2019) The liver tumor segmentation benchmark (LiTS). http:\/\/arxiv.org\/abs\/1901.04056"},{"key":"6750_CR8","unstructured":"Brendan McMahan H, Moore E, Ramage D, Hampson S, Ag\u00fcera y Arcas B (2017) Communication-efficient learning of deep networks from decentralized data. In: Proceedings of the 20th international conference on artificial intelligence and statistics, AISTATS 2017. http:\/\/arxiv.org\/abs\/1602.05629"},{"key":"6750_CR9","doi-asserted-by":"crossref","unstructured":"Cai H, Chen T, Zhang W, Yu Y, Wang J (2018) Efficient architecture search by network transformation. In: 32nd AAAI conference on artificial intelligence, AAAI 2018, pp 2787\u20132794","DOI":"10.1609\/aaai.v32i1.11709"},{"key":"6750_CR10","doi-asserted-by":"publisher","unstructured":"Chang CY, Chung PC, Hong YC, Tseng CH (2011) A neural network for thyroid segmentation and volume estimation in CT images. IEEE Computat Intell Mag 6(4):43\u201355. https:\/\/doi.org\/10.1109\/MCI.2011.942756.. https:\/\/ieeexplore.ieee.org\/document\/6052365","DOI":"10.1109\/MCI.2011.942756."},{"key":"6750_CR11","unstructured":"Chen S, Bruijne MD (2018) An end-to-end approach to semantic segmentation with 3D CNN and posterior-CRF in medical images. http:\/\/arxiv.org\/abs\/1811.03549"},{"key":"6750_CR12","unstructured":"Chen Y, Li J, Xiao H, Jin X, Yan S, Feng J (2017) Dual path networks. In: Adv Neural Inf Process Syst 2017:4468\u20134476"},{"key":"6750_CR13","unstructured":"Chetlur S, Woolley C, Vandermersch P, Cohen J, Tran J, Catanzaro B, Shelhamer E (2014) cuDNN: efficient primitives for deep learning. arXiv: Neural and evolutionary computing. http:\/\/arxiv.org\/abs\/1410.0759"},{"key":"6750_CR14","unstructured":"Choi Y, El-Khamy M, Lee J (2017) Towards the limit of network quantization. In: 5th International conference on learning representations, ICLR 2017\u2014conference track proceedings. http:\/\/arxiv.org\/abs\/1612.01543"},{"key":"6750_CR15","unstructured":"Crankshaw D, Sela GE, Mo S, Zumar C, Gonzalez JE, Stoica I, Tumanov A (2018) InferLine: ML prediction pipeline provisioning and management for tight latency objectives. http:\/\/arxiv.org\/abs\/1812.01776"},{"key":"6750_CR16","unstructured":"Czarnecki WM, Osindero S, Jaderberg M, Swirszcz G, Pascanu R (2017) Sobolev training for neural networks. Adv Neural Inf Process Syst 2017:4279\u20134288"},{"key":"6750_CR17","doi-asserted-by":"publisher","unstructured":"Deng J, Dong W, Socher R, Li LJ (2010) Kai Li, Li Fei-Fei: ImageNet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, pp 248\u2013255. IEEE. https:\/\/doi.org\/10.1109\/cvpr.2009.5206848.https:\/\/ieeexplore.ieee.org\/document\/5206848\/","DOI":"10.1109\/cvpr.2009.5206848."},{"issue":"January","key":"6750_CR18","first-page":"1269","volume":"2","author":"E Denton","year":"2014","unstructured":"Denton E, Zaremba W, Bruna J, LeCun Y, Fergus R (2014) Exploiting linear structure within convolutional networks for efficient evaluation. Adv Neural Inf Process Syst 2(January):1269\u20131277","journal-title":"Adv Neural Inf Process Syst"},{"key":"6750_CR19","unstructured":"Dettmers T (2016) 8-Bit approximations for parallelism in deep learning. In: 4th International conference on learning representations, ICLR 2016\u2014conference track proceedings"},{"key":"6750_CR20","doi-asserted-by":"publisher","unstructured":"Dolz J, Gopinath K, Yuan J, Lombaert H, Desrosiers C, Ben Ayed I (2019) HyperDense-net: a hyper-densely connected cnn for multi-modal image segmentation. IEEE Trans Med Imag 38(5):1116\u20131126. https:\/\/doi.org\/10.1109\/TMI.2018.2878669.. https:\/\/arxiv.org\/abs\/1804.02967","DOI":"10.1109\/TMI.2018.2878669."},{"key":"6750_CR21","doi-asserted-by":"publisher","unstructured":"Dou Q, Chen H, Jin Y, Yu L, Qin J, Heng PA (2016) 3D deeply supervised network for automatic liver segmentation from CT volumes. In: S.\u00a0Ourselin, L.\u00a0Joskowicz, M.R. Sabuncu, G.\u00a0Unal, W.\u00a0Wells (eds.) Lecture notes in computer science (including subseries lecture notes in Artificial intelligence and lecture notes in bioinformatics), vol 9901 LNCS, pp 149\u2013157. Springer International Publishing, Cham. https:\/\/doi.org\/10.1007\/978-3-319-46723-8_18","DOI":"10.1007\/978-3-319-46723-8_18"},{"key":"6750_CR22","doi-asserted-by":"publisher","unstructured":"Fischl B (2012). FreeSurfer. https:\/\/doi.org\/10.1016\/j.neuroimage. 21 Jan 2012. URL: http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC3685476\/","DOI":"10.1016\/j.neuroimage"},{"key":"6750_CR23","unstructured":"Gao Y, Yang H, Zhang P, Zhou C, Hu Y (2019) GraphNAS: graph neural architecture search with reinforcement learning. In: arXiv, vol. abs\/1611.0"},{"key":"6750_CR24","doi-asserted-by":"publisher","unstructured":"Gibson E, Li W, Sudre C, Fidon L, Shakir DI, Wang G, Eaton-Rosen Z, Gray R, Doel T, Hu Y, Whyntie T, Nachev P, Modat M, Barratt DC, Ourselin S, Cardoso MJ, Vercauteren T (2018) NiftyNet: a deep-learning platform for medical imaging. Comput Methods Programs Biomed 158:113\u2013122. https:\/\/doi.org\/10.1016\/j.cmpb.2018.01.025.. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0169260717311823","DOI":"10.1016\/j.cmpb.2018.01.025."},{"key":"6750_CR25","doi-asserted-by":"crossref","unstructured":"Goodfellow IJ, Erhan D, Luc Carrier P, Courville A, Mirza M, Hamner B, Cukierski W, Tang Y, Thaler D, Lee DH, Zhou Y, Ramaiah C, Feng F, Li R, Wang X, Athanasakis D, Shawe-Taylor J, Milakov M, Park J, Ionescu R, Popescu M, Grozea C, Bergstra J, Xie J, Romaszko L, Xu B, Chuang Z, Bengio Y (2015) Challenges in representation learning: a report on three machine learning contests. Neural Netw 64:59\u201363","DOI":"10.1016\/j.neunet.2014.09.005"},{"key":"6750_CR26","unstructured":"Graham, S., Vu, Q.D., Ahmed Raza, S.E., Azam, A., Tsang, Y.W., Kwak, J.T., Rajpoot, N.: HoVer-Net: simultaneous segmentation and classification of nuclei in multi-tissue histology images, 1\u201311 (2018). http:\/\/arxiv.org\/abs\/1812.06499"},{"issue":"2","key":"6750_CR27","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1109\/TPAMI.2018.2844175","volume":"42","author":"K He","year":"2020","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2020) Mask R-CNN. IEEE Tran Pattern Anal Mach Intell 42(2):386\u2013397. https:\/\/doi.org\/10.1109\/TPAMI.2018.2844175","journal-title":"IEEE Tran Pattern Anal Mach Intell"},{"key":"6750_CR28","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition 2016:770\u201377. https:\/\/doi.org\/10.1109\/CVPR.2016.90http:\/\/arxiv.org\/abs\/1512.03385","DOI":"10.1109\/CVPR.2016.90"},{"key":"6750_CR29","doi-asserted-by":"crossref","unstructured":"Henschel L, Conjeti S, Estrada S, Diers K, Fischl B, Reuter M (2020) FastSurfer\u2014a fast and accurate deep learning based neuroimaging pipeline. NeuroImage 219. Di: 10.1016\/j.neuroimage.2020.117012. http:\/\/arxiv.org\/abs\/1910.03866","DOI":"10.1016\/j.neuroimage.2020.117012"},{"key":"6750_CR30","doi-asserted-by":"crossref","unstructured":"Hohman F, Kahng M, Pienta R, Chau DH (2019) Visual analytics in deep learning: an interrogative survey for the next frontiers. IEEE Trans Vis Comput Graph 25(8):2674\u20132693","DOI":"10.1109\/TVCG.2018.2843369"},{"key":"6750_CR31","doi-asserted-by":"publisher","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings\u201430th IEEE conference on computer vision and pattern recognition, CVPR 2017, vol 2017, pp 2261\u20132269 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.243.http:\/\/arxiv.org\/abs\/1608.06993","DOI":"10.1109\/CVPR.2017.243."},{"key":"6750_CR32","unstructured":"Hykes S (2013) Empowering app development for developers | Docker. https:\/\/www.docker.com\/"},{"key":"6750_CR33","doi-asserted-by":"publisher","first-page":"13","DOI":"10.3389\/fninf.2014.00013","volume":"8","author":"M McCormick","year":"2014","unstructured":"McCormick M, Liu X, Jomier J, Marion C, Ibanez L (2014) ITK: enabling reproducible research and open science. Front Neuroinform 8:13. https:\/\/doi.org\/10.3389\/fninf.2014.00013","journal-title":"Front Neuroinform"},{"key":"6750_CR34","doi-asserted-by":"crossref","unstructured":"Jamaludin A, Kadir T, Zisserman A (2017) SpineNet: Automated classification and evidence visualization in spinal MRIs. Med Image Anal 41:63\u201373","DOI":"10.1016\/j.media.2017.07.002"},{"key":"6750_CR35","doi-asserted-by":"crossref","unstructured":"Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM (2012) FSL\u2014review. NeuroImage 62(2):782\u201390","DOI":"10.1016\/j.neuroimage.2011.09.015"},{"key":"6750_CR36","doi-asserted-by":"publisher","unstructured":"Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: Convolutional architecture for fast feature embedding. In: MM 2014\u2014Proceedings of the 2014 ACM conference on multimedia, pp 675\u2013678. https:\/\/doi.org\/10.1145\/2647868.2654889","DOI":"10.1145\/2647868.2654889"},{"key":"6750_CR37","doi-asserted-by":"publisher","unstructured":"Jimenez-Carretero D, Bermejo-Pel\u00e1ez D, Nardelli P, Fraga P, Fraile E, San Jos\u00e9 Est\u00e9par R, Ledesma-Carbayo MJ (2019) A graph-cut approach for pulmonary artery-vein segmentation in noncontrast CT images. Med Image Anal 52:144\u2013159. https:\/\/doi.org\/10.1016\/j.media.2018.11.011. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S1361841518308740","DOI":"10.1016\/j.media.2018.11.011"},{"key":"6750_CR38","doi-asserted-by":"crossref","unstructured":"Kamnitsas K, Ledig C, Newcombe VF, 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","DOI":"10.1016\/j.media.2016.10.004"},{"key":"6750_CR39","doi-asserted-by":"publisher","unstructured":"Khagi B, Lee CG, Kwon GR (2019) Alzheimer\u2019s disease classification from brain MRI based on transfer learning from CNN. In: BMEiCON 2018\u201411th biomedical engineering international conference. https:\/\/doi.org\/10.1109\/BMEiCON.2018.8609974","DOI":"10.1109\/BMEiCON.2018.8609974"},{"key":"6750_CR40","unstructured":"Khvostikov A, Aderghal K, Benois-Pineau J, Krylov A, Catheline G (2018) 3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies. http:\/\/arxiv.org\/abs\/1801.05968"},{"key":"6750_CR41","unstructured":"Khvostikov A, Benois-Pineau J, Krylov A, Catheline G (2017) Classification methods on different brain imaging modalities for Alzheimer disease studies. In: GraphiCon 2017\u201427th international conference on computer graphics and vision, pp 237\u2013242"},{"key":"6750_CR42","unstructured":"Kone\u010dn\u00fd J, McMahan HB, Yu FX, Richt\u00e1rik P, Suresh AT, Bacon D ( 2016) Federated learning: strategies for improving communication efficiency. http:\/\/arxiv.org\/abs\/1610.05492"},{"key":"6750_CR43","doi-asserted-by":"publisher","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. In: Commun ACM 60:84\u201390. https:\/\/doi.org\/10.1145\/3065386","DOI":"10.1145\/3065386"},{"issue":"7","key":"6750_CR44","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1109\/TMI.2017.2677499","volume":"36","author":"N Kumar","year":"2017","unstructured":"Kumar N, Verma R, Sharma S, Bhargava S, Vahadane A, Sethi A (2017) A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE Trans Med Imag 36(7):1550\u20131560. https:\/\/doi.org\/10.1109\/TMI.2017.2677499","journal-title":"IEEE Trans Med Imag"},{"key":"6750_CR45","doi-asserted-by":"publisher","unstructured":"Lebedev V, Lempitsky V (2016) Fast convnets using group-wise brain damage. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition 2016:2554\u20132564. https:\/\/doi.org\/10.1109\/CVPR.2016.280","DOI":"10.1109\/CVPR.2016.280"},{"issue":"11","key":"6750_CR46","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132323. https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc IEEE"},{"key":"6750_CR47","doi-asserted-by":"publisher","unstructured":"Lee LK, Liew SC (2015) A survey of medical image processing tools. In: 2015 4th international conference on software engineering and computer systems, ICSECS 2015: virtuous software solutions for big data, pp 171\u2013176. https:\/\/doi.org\/10.1109\/ICSECS.2015.7333105","DOI":"10.1109\/ICSECS.2015.7333105"},{"issue":"12","key":"6750_CR48","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","volume":"40","author":"Z Li","year":"2018","unstructured":"Li Z, Hoiem D (2018) Learning without forgetting. IEEE Trans Pattern Anal Mach Intell 40(12):2935\u20132947. https:\/\/doi.org\/10.1109\/TPAMI.2017.2773081","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6750_CR49","doi-asserted-by":"crossref","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JA, van Ginneken B, S\u00e1nchez CI (2017). A survey on deep learning in medical image analysis","DOI":"10.1016\/j.media.2017.07.005"},{"key":"6750_CR50","doi-asserted-by":"publisher","unstructured":"Litjens, G., S\u00e1nchez CI, Timofeeva N, Hermsen M, Nagtegaal I, Kovacs I, Hulsbergen-Van De Kaa C, Bult P, Van Ginneken B, Van Der Laak J (2016) Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep 6(1):26286. https:\/\/doi.org\/10.1038\/srep26286.http:\/\/www.nature.com\/articles\/srep26286","DOI":"10.1038\/srep26286."},{"key":"6750_CR51","doi-asserted-by":"crossref","unstructured":"Liu T, Guo Q, Lian C, Ren X, Liang S, Yu J, Niu L, Sun W, Shen D (2019) Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks. Med Image Anal 58:101555","DOI":"10.1016\/j.media.2019.101555"},{"key":"6750_CR52","doi-asserted-by":"publisher","unstructured":"Lowekamp BC, Chen DT, Ib\u00e1\u00f1ez L, Blezek D (2013) The design of simpleITK. Front Neuroinf 7(DEC):45. https:\/\/doi.org\/10.3389\/fninf.2013.00045","DOI":"10.3389\/fninf.2013.00045"},{"key":"6750_CR53","doi-asserted-by":"crossref","unstructured":"Lu F, Wu F, Hu P, Peng Z, Kong D (2017) Automatic 3D liver location and segmentation via convolutional neural network and graph cut. Int J Comput Assist Radiol Surg 12(2):171\u2013182","DOI":"10.1007\/s11548-016-1467-3"},{"key":"6750_CR54","unstructured":"Magee D, Treanor D, Crellin D, Shires M, Smith K, Mohee K, Quirke P (2009) Colour normalisation in digital histopathology images. Opt Tissue Image Anal Microsc Histopathol Endosc MICCAI Workshop, pp 100\u2013111. https:\/\/www.researchgate.net\/publication\/228855426_Colour_Normalisation_in_Digital_Histopathology_Imageshttps:\/\/www.researchgate.net\/publication\/339593324_Colour_Normalisation_in_Digital_Histopathology_Images"},{"issue":"4","key":"6750_CR55","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1145\/1868358.1868363","volume":"10","author":"J Maloney","year":"2010","unstructured":"Maloney J, Resnick M, Rusk N, Silverman B, Eastmond E (2010) The scratch programming language and environment. ACM Trans Comput Educ 10(4):16. https:\/\/doi.org\/10.1145\/1868358.1868363","journal-title":"ACM Trans Comput Educ"},{"key":"6750_CR56","unstructured":"Marcos Romero BS (2019) Blueprints visual scripting for unreal engine. https:\/\/docs.unrealengine.com\/en-US\/Engine\/Blueprints\/index.html"},{"key":"6750_CR57","unstructured":"Marlow S (2010) Haskell 2010 language report. Language, p 329. http:\/\/haskell.org\/definition\/haskell2010.pdf"},{"key":"6750_CR58","doi-asserted-by":"publisher","unstructured":"Milletari F, Navab N, Ahmadi SA (2016) V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: Proceedings\u20142016 4th international conference on 3D vision, 3DV 2016, pp 565\u2013571. https:\/\/doi.org\/10.1109\/3DV.2016.79","DOI":"10.1109\/3DV.2016.79"},{"key":"6750_CR59","doi-asserted-by":"publisher","unstructured":"Minati, L., Edginton, T., Grazia Bruzzone, M., Giaccone, G.: Reviews: current concepts in alzheimer\u2019s disease: a multidisciplinary review (2009). https:\/\/doi.org\/10.1177\/1533317508328602","DOI":"10.1177\/1533317508328602"},{"key":"6750_CR60","doi-asserted-by":"publisher","unstructured":"Moradi M, Madani A, Karargyris A, Syeda-Mahmood TF (2018) Chest x-ray generation and data augmentation for cardiovascular abnormality classification. In: E.D. Angelini, B.A. Landman (eds.) Medical imaging 2018: image processing 10574:57. SPIE. https:\/\/doi.org\/10.1117\/12.2293971.https:\/\/doi.org\/10.1117\/12.2293971","DOI":"10.1117\/12.2293971."},{"issue":"4","key":"6750_CR61","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1016\/j.nic.2005.09.008","volume":"15","author":"SG Mueller","year":"2005","unstructured":"Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack C, Jagust W, Trojanowski JQ, Toga AW, Beckett L (2005) The Alzheimer\u2019s disease neuroimaging initiative. Neuroimag Clin North Am 15(4):869\u2013877. https:\/\/doi.org\/10.1016\/j.nic.2005.09.008","journal-title":"Neuroimag Clin North Am"},{"key":"6750_CR62","doi-asserted-by":"crossref","unstructured":"M\u00fcller, D., Kramer, F.: MIScnn: A framework for medical image segmentation with convolutional neural networks and deep learning (2019)","DOI":"10.24926\/548719.074"},{"issue":"2","key":"6750_CR63","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1109\/TMI.2018.2865709","volume":"38","author":"P Naylor","year":"2019","unstructured":"Naylor P, La\u00e9 M, Reyal F, Walter T (2019) Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE Trans Med Imag 38(2):448\u2013459. https:\/\/doi.org\/10.1109\/TMI.2018.2865709","journal-title":"IEEE Trans Med Imag"},{"key":"6750_CR64","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/978-3-540-75402-2_4","volume":"84","author":"MR Ogiela","year":"2008","unstructured":"Ogiela MR, Tadeusiewicz R (2008) Preprocessing medical images and their overall enhancement. Stud Comput Intell 84:65\u201397. https:\/\/doi.org\/10.1007\/978-3-540-75402-2_4","journal-title":"Stud Comput Intell"},{"key":"6750_CR65","unstructured":"Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in $$\\backslash$$uppercasePy$$\\backslash$$uppercaseTorch. In: NIPS 2017 Autodiff Workshop: the future of gradient-based machine learning software and techniques, pp 8024\u20138035 (2017). http:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf"},{"key":"6750_CR66","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, K\u00f6pf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2019) PyTorch: An imperative style, high-performance deep learning library. http:\/\/arxiv.org\/abs\/1912.01703"},{"issue":"5","key":"6750_CR67","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1109\/TMI.2016.2538465","volume":"35","author":"S Pereira","year":"2016","unstructured":"Pereira S, Pinto A, Alves V, Silva CA (2016) Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imag 35(5):1240\u20131251. https:\/\/doi.org\/10.1109\/TMI.2016.2538465","journal-title":"IEEE Trans Med Imag"},{"key":"6750_CR68","unstructured":"Pham H, Guan MY, Zoph B, Le QV, Dean J (2018) Efficient neural architecture search via parameter sharing. In: 35th International conference on machine learning, ICML 2018, vol \u00a09, pp 6522\u20136531"},{"key":"6750_CR69","doi-asserted-by":"crossref","unstructured":"Qaiser T, Tsang YW, Taniyama D, Sakamoto N, Nakane K, Epstein D, Rajpoot N (2019) Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features. Med Image Anal 55:1\u201314","DOI":"10.1016\/j.media.2019.03.014"},{"issue":"5","key":"6750_CR70","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1023\/A:1012052928198","volume":"9","author":"T Radul","year":"2001","unstructured":"Radul T (2001) Functional representations of Lawson monads. Appl Categor Struct 9(5):457\u2013463. https:\/\/doi.org\/10.1023\/A:1012052928198","journal-title":"Appl Categor Struct"},{"key":"6750_CR71","unstructured":"Rajan, D., Beymer, D., Abedin, S., Dehghan, E.: Pi-PE: A pipeline for pulmonary embolism detection using sparsely annotated 3D CT images (2019). http:\/\/arxiv.org\/abs\/1910.02175"},{"key":"6750_CR72","unstructured":"Rajchl, M., Pawlowski, N., Rueckert, D., Matthews, P.M., Glocker, B.: NeuroNet: Fast and robust reproduction of multiple brain image segmentation pipelines (2018). http:\/\/arxiv.org\/abs\/1806.04224"},{"key":"6750_CR73","doi-asserted-by":"publisher","unstructured":"Rameshkumar S, Thilak JAJ, Suresh P, Sathishkumar S, Subramani N (2016) Speckle noise removal in MRI scan image using WB\u2014filter. Int J Innov Res Sci Eng Technol 5(12):21079\u201321083. https:\/\/doi.org\/10.15680\/IJIRSET.2016.0512161","DOI":"10.15680\/IJIRSET.2016.0512161"},{"issue":"5","key":"6750_CR74","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/38.946629","volume":"21","author":"E Reinhard","year":"2001","unstructured":"Reinhard E, Ashikhmin M, Gooch B, Shirley P (2001) Color transfer between images. IEEE Comput Graph Appl 21(5):34\u201341. https:\/\/doi.org\/10.1109\/38.946629","journal-title":"IEEE Comput Graph Appl"},{"key":"6750_CR75","doi-asserted-by":"publisher","unstructured":"Rezaei M, Shahidi M (2020) Zero-shot learning and its applications from autonomous vehicles to covid-19 diagnosis: a review. https:\/\/doi.org\/10.1016\/j.ibmed.2020.100005.. http:\/\/arxiv.org\/abs\/2004.14143","DOI":"10.1016\/j.ibmed.2020.100005."},{"key":"6750_CR76","doi-asserted-by":"publisher","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Lecture notes in computer science (including subseries Lecture Notes in Artificial intelligence and lecture notes in bioinformatics), vol 9351, pp 234\u2013241 (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"4","key":"6750_CR77","first-page":"291","volume":"23","author":"AC Ruifrok","year":"2001","unstructured":"Ruifrok AC, Johnston DA (2001) Quantification of histochemical staining by color deconvolution. Anal Quant Cytol Histol 23(4):291\u2013299","journal-title":"Anal Quant Cytol Histol"},{"key":"6750_CR78","unstructured":"Ryan Olson, Jonathan Calmels, F.A., |, P.R.: NVIDIA Docker: GPU server application deployment made easy (2016). https:\/\/devblogs.nvidia.com\/nvidia-docker-gpu-server-application-deployment-made-easy\/"},{"issue":"1","key":"6750_CR79","doi-asserted-by":"publisher","first-page":"27","DOI":"10.4103\/2153-3539.119005","volume":"4","author":"M Satyanarayanan","year":"2013","unstructured":"Satyanarayanan M, Goode A, Gilbert B, Harkes J, Jukic D (2013) OpenSlide: a vendor-neutral software foundation for digital pathology. J Pathol Inf 4(1):27. https:\/\/doi.org\/10.4103\/2153-3539.119005","journal-title":"J Pathol Inf"},{"issue":"3","key":"6750_CR80","first-page":"243","volume":"5","author":"S Senthilraja","year":"2014","unstructured":"Senthilraja S, Suresh P, Suganthi M (2014) Noise reduction in computed tomography image using WB-filter. Int J Sci Eng Res 5(3):243","journal-title":"Int J Sci Eng Res"},{"key":"6750_CR81","doi-asserted-by":"publisher","unstructured":"Setio AAA, Traverso A, de\u00a0Bel T, Berens MS, van\u00a0den Bogaard C, Cerello P, Chen H, Dou Q, Fantacci ME, Geurts B, van\u00a0der Gugten R, Heng PA, Jansen B, de\u00a0Kaste MM, Kotov V, Lin JYH, Manders JT, S\u00f3\u00f1ora-Mengana A, Garc\u00eda-Naranjo JC, Papavasileiou E, Prokop M, Saletta M, Schaefer-Prokop CM, Scholten ET, Scholten L, Snoeren MM, Torres EL, Vandemeulebroucke J, Walasek N, Zuidhof GC, van Ginneken B, Jacobs C (2017) Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Med Image Anal 42:1\u201313. https:\/\/doi.org\/10.1016\/j.media.2017.06.015.http:\/\/arxiv.org\/abs\/1612.08012","DOI":"10.1016\/j.media.2017.06.015."},{"key":"6750_CR82","doi-asserted-by":"publisher","unstructured":"Sheller MJ, Reina GA, Edwards B, Martin J, Bakas S (2019) Multi-institutional deep learning modeling without sharing patient data: a feasibility study on brain tumor segmentation. In: Lecture notes in Computer science (including subseries Lecture notes in Artificial intelligence and lecture notes in bioinformatics), vol 11383 LNCS, pp 92\u2013104. https:\/\/doi.org\/10.1007\/978-3-030-11723-8_9","DOI":"10.1007\/978-3-030-11723-8_9"},{"issue":"1","key":"6750_CR83","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):60. https:\/\/doi.org\/10.1186\/s40537-019-0197-0","journal-title":"J Big Data"},{"key":"6750_CR84","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: 3rd International conference on learning representations, ICLR 2015\u2014conference track proceedings"},{"key":"6750_CR85","unstructured":"Skibbe H, Watakabe A, Nakae K, Gutierrez CE, Tsukada H, Hata J, Kawase T, Gong R, Woodward A, Doya K, Okano H, Yamamori T, Ishii S (2019) MarmoNet: a pipeline for automated projection mapping of the common marmoset brain from whole-brain serial two-photon tomography. http:\/\/arxiv.org\/abs\/1908.00876"},{"key":"6750_CR86","doi-asserted-by":"crossref","unstructured":"Song J, Xiao L, Molaei M, Lian Z (2019) Multi-layer boosting sparse convolutional model for generalized nuclear segmentation from histopathology images. Knowl Based Syst 176:40\u201353","DOI":"10.1016\/j.knosys.2019.03.031"},{"key":"6750_CR87","doi-asserted-by":"publisher","unstructured":"Swiderska-Chadaj Z, Pinckaers H, van Rijthoven M, Balkenhol M, Melnikova M, Geessink O, Manson Q, Sherman M, Polonia A, Parry J, Abubakar M, Litjens G, van\u00a0der Laak J, Ciompi F (2019) Learning to detect lymphocytes in immunohistochemistry with deep learning. Med Image Anal 58. https:\/\/doi.org\/10.1016\/j.media.2019.101547","DOI":"10.1016\/j.media.2019.101547"},{"key":"6750_CR88","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-ResNet and the impact of residual connections on learning. In: 31st AAAI conference on artificial intelligence, AAAI 2017, pp 4278\u20134284 (2017)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"6750_CR89","unstructured":"Tai, C., Xiao, T., Zhang, Y., Wang, X., Weinan, E.: Convolutional neural networks with low-rank regularization. In: 4th International conference on learning representations, ICLR 2016\u2014conference track proceedings (2016)"},{"key":"6750_CR90","unstructured":"The Linux foundation: production-grade container orchestration\u2014Kubernetes (2020). https:\/\/kubernetes.io\/"},{"issue":"9","key":"6750_CR91","first-page":"4373","volume":"2","author":"R Thenua","year":"2010","unstructured":"Thenua R, Agarwal S (2010) Simulation and performance analysis of adaptive filter in noise cancellation. Int J Eng Sci Technol 2(9):4373\u20134378","journal-title":"Int J Eng Sci Technol"},{"issue":"9","key":"6750_CR92","doi-asserted-by":"publisher","first-page":"2047","DOI":"10.1109\/TMI.2019.2895318","volume":"38","author":"M Tofighi","year":"2019","unstructured":"Tofighi M, Guo T, Vanamala JK, Monga V (2019) Prior information guided regularized deep learning for cell nucleus detection. IEEE Trans Med Imag 38(9):2047\u20132058. https:\/\/doi.org\/10.1109\/TMI.2019.2895318","journal-title":"IEEE Trans Med Imag"},{"key":"6750_CR93","doi-asserted-by":"publisher","unstructured":"Trullo, R., Petitjean, C., Ruan, S., Dubray, B., Nie, D., Shen, D.: Segmentation of organs at risk in thoracic CT images using a sharpmask architecture and conditional random fields. In: Proceedings\u2014international symposium on biomedical imaging, vol 2017, pp 1003\u20131006 (2017). https:\/\/doi.org\/10.1109\/ISBI.2017.7950685","DOI":"10.1109\/ISBI.2017.7950685"},{"issue":"6","key":"6750_CR94","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","volume":"29","author":"NJ Tustison","year":"2010","unstructured":"Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imag 29(6):1310\u20131320. https:\/\/doi.org\/10.1109\/TMI.2010.2046908","journal-title":"IEEE Trans Med Imag"},{"issue":"8","key":"6750_CR95","doi-asserted-by":"publisher","first-page":"1962","DOI":"10.1109\/TMI.2016.2529665","volume":"35","author":"A Vahadane","year":"2016","unstructured":"Vahadane A, Peng T, Sethi A, Albarqouni S, Wang L, Baust M, Steiger K, Schlitter AM, Esposito I, Navab N (2016) Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans Med Imag 35(8):1962\u20131971. https:\/\/doi.org\/10.1109\/TMI.2016.2529665","journal-title":"IEEE Trans Med Imag"},{"key":"6750_CR96","unstructured":"Vanhoucke V, Senior A, Mao M (2011) Improving the speed of neural networks on CPUs. Proc Deep Learn, pp 1\u20138. http:\/\/research.google.com\/pubs\/archive\/37631.pdf"},{"key":"6750_CR97","doi-asserted-by":"crossref","unstructured":"Wang Z, Lin Y, Cheng KTT, Yang X (2020) Semi-supervised mp-MRI data synthesis with StitchLayer and auxiliary distance maximization. Med Image Anal 59:101565","DOI":"10.1016\/j.media.2019.101565"},{"key":"6750_CR98","doi-asserted-by":"publisher","unstructured":"Wu Z, Song S, Khosla A, Yu F, Zhang L, Tang X, Xiao J (2015) 3D ShapeNets: a deep representation for volumetric shapes. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, vol 07\u201312-June, pp 1912\u20131920. https:\/\/doi.org\/10.1109\/CVPR.2015.7298801.http:\/\/arxiv.org\/abs\/1406.5670","DOI":"10.1109\/CVPR.2015.7298801."},{"issue":"1","key":"6750_CR99","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1007\/s11390-017-1709-z","volume":"32","author":"GL Yao","year":"2017","unstructured":"Yao GL (2017) A survey on pre-processing in image matting. J Comput Sci Technol 32(1):122\u2013138. https:\/\/doi.org\/10.1007\/s11390-017-1709-z","journal-title":"J Comput Sci Technol"},{"key":"6750_CR100","doi-asserted-by":"crossref","unstructured":"Yi X, Walia E, Babyn P (2019) Generative adversarial network in medical imaging: a review. Med Image Anal 58:101552","DOI":"10.1016\/j.media.2019.101552"},{"key":"6750_CR101","doi-asserted-by":"crossref","unstructured":"Yong CY, Chew KM, Mahmood NH, Ariffin I (2012) A survey of visualization tools in medical imaging. Proc Soc Behav Sci 56:265\u2013271","DOI":"10.1016\/j.sbspro.2012.09.654"},{"key":"6750_CR102","unstructured":"Zhang J, Gajjala S, Agrawal P, Tison GH, Hallock LA Beussink-Nelson L, Lassen MH, Fan E, Aras MA, Jordan CR, Fleischmann KE, Melisko M, Qasim A, Efros A, Shah SJ, Bajcsy R, Deo RC (2017) A computer vision pipeline for automated determination of cardiac structure and function and detection of disease by two-dimensional echocardiography. http:\/\/arxiv.org\/abs\/1706.07342"},{"key":"6750_CR103","doi-asserted-by":"crossref","unstructured":"Zhang, K., Snavely, N., Sun, J.: Leveraging vision reconstruction pipelines for satellite imagery (2019). http:\/\/arxiv.org\/abs\/1910.02989","DOI":"10.1109\/ICCVW.2019.00269"},{"key":"6750_CR104","doi-asserted-by":"publisher","unstructured":"shi Zhang, Q., chun Zhu, S.: Visual interpretability for deep learning: a survey (2018). https:\/\/doi.org\/10.1631\/FITEE.1700808","DOI":"10.1631\/FITEE.1700808"},{"key":"6750_CR105","doi-asserted-by":"publisher","unstructured":"Zhou Y, Xie L, Shen W, Wang Y, Fishman EK, Yuille AL (2017) A fixed-point model for pancreas segmentation in abdominal CT scans. In: M.\u00a0Descoteaux, L.\u00a0Maier-Hein, A.\u00a0Franz, P.\u00a0Jannin, D.L. Collins, S.\u00a0Duchesne (eds.) Lecture notes in Computer science (including subseries Lecture notes in Artificial intelligence and lecture notes in Bioinformatics), vol 10433 LNCS, pp693\u2013701. Springer International Publishing, Cham. https:\/\/doi.org\/10.1007\/978-3-319-66182-7_79","DOI":"10.1007\/978-3-319-66182-7_79"},{"key":"6750_CR106","doi-asserted-by":"publisher","unstructured":"Zhu, W., Liu, C., Fan, W., Xie, X.: DeepLung: Deep 3D dual path nets for automated pulmonary nodule detection and classification. In: Proceedings\u20142018 IEEE winter conference on applications of computer vision, WACV 2018, 2018:673\u2013681 (2018). https:\/\/doi.org\/10.1109\/WACV.2018.00079","DOI":"10.1109\/WACV.2018.00079"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06750-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06750-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06750-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T10:46:03Z","timestamp":1744195563000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06750-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,16]]},"references-count":106,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["6750"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06750-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,16]]},"assertion":[{"value":"3 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}