{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T01:17:23Z","timestamp":1767921443630,"version":"3.49.0"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2020,9,5]],"date-time":"2020-09-05T00:00:00Z","timestamp":1599264000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,9,5]],"date-time":"2020-09-05T00:00:00Z","timestamp":1599264000000},"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":["Grant 61671152"],"award-info":[{"award-number":["Grant 61671152"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["Grant 61901119"],"award-info":[{"award-number":["Grant 61901119"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1007\/s13042-020-01194-4","type":"journal-article","created":{"date-parts":[[2020,9,5]],"date-time":"2020-09-05T05:02:35Z","timestamp":1599282155000},"page":"651-660","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Multipath feature recalibration DenseNet for image classification"],"prefix":"10.1007","volume":"12","author":[{"given":"Bolin","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiesong","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahui","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5900-4175","authenticated-orcid":false,"given":"Liqun","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,5]]},"reference":[{"key":"1194_CR1","first-page":"1097","volume-title":"Imagenet classification with deep convolutional neural networks. Neural information processing systems","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Neural information processing systems. Springer, New York, pp 1097\u20131105"},{"key":"1194_CR2","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: International conference on learning representations (ICLR)"},{"key":"1194_CR3","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition(CVPR), pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1194_CR4","first-page":"2377","volume-title":"Training very deep networks. Advances in neural information processing systems","author":"RK Srivastava","year":"2015","unstructured":"Srivastava RK, Greff K, Schmidhuber J (2015) Training very deep networks. Advances in neural information processing systems. Springer, New York, pp 2377\u20132385"},{"key":"1194_CR5","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"02","key":"1194_CR6","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1142\/S0218488598000094","volume":"06","author":"S Hochreiter","year":"1998","unstructured":"Hochreiter S (1998) The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int J Uncertain Fuzziness Knowl Based Syst 06(02):107\u2013116","journal-title":"Int J Uncertain Fuzziness Knowl Based Syst"},{"issue":"02","key":"1194_CR7","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","volume":"05","author":"Y Bengio","year":"2002","unstructured":"Bengio Y (2002) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw Learn Syst 05(02):157\u2013166","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1194_CR8","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Laurens VDM, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"1194_CR9","unstructured":"Liu W, Zeng K (2018) Sparsenet: a sparse DenseNet for image classification, arXiv. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)"},{"key":"1194_CR10","doi-asserted-by":"publisher","first-page":"9872","DOI":"10.1109\/ACCESS.2018.2890127","volume":"7","author":"K Zhang","year":"2019","unstructured":"Zhang K, Guo Y, Wang X, Yuan J, Ding Q (2019) Multiple feature reweight DenseNet for image classification. IEEE Access 7:9872\u20139880","journal-title":"IEEE Access"},{"issue":"6","key":"1194_CR11","doi-asserted-by":"publisher","first-page":"1407","DOI":"10.1109\/TMI.2018.2823338","volume":"37","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Liang X, Dong X, Xie Y, Cao G (2018) A sparse-view CT reconstruction method based on combination of DenseNet and deconvolution. IEEE Trans Med Imaging 37(6):1407\u20131417","journal-title":"IEEE Trans Med Imaging"},{"key":"1194_CR12","unstructured":"Chen Y, Li J, Xiao H, Jin X, Yan S, Feng J (2017) Dual path networks. In: Neural information processing systems (NeurIPS), pp 4467\u20134475"},{"key":"1194_CR13","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.ins.2019.01.012","volume":"482","author":"B Lodhi","year":"2019","unstructured":"Lodhi B, Kang J (2019) Multipath-densenet: a supervised ensemble architecture of densely connected convolutional networks. Inf Sci 482:63\u201372","journal-title":"Inf Sci"},{"key":"1194_CR14","doi-asserted-by":"crossref","unstructured":"Huang G, Liu S, Der Maaten LV, Weinberger KQ (2018) CondenseNet: an efficient DenseNet using learned group convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 2752\u20132761","DOI":"10.1109\/CVPR.2018.00291"},{"key":"1194_CR15","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning (ICML), pp 448\u2013456"},{"key":"1194_CR16","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: International conference on artificial intelligence and statistics, pp 315\u2013323"},{"issue":"11","key":"1194_CR17","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\u20132324","journal-title":"Proc IEEE"},{"key":"1194_CR18","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1194_CR19","unstructured":"Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images, Tech Report"},{"issue":"6","key":"1194_CR20","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1109\/MSP.2012.2211477","volume":"29","author":"L Deng","year":"2012","unstructured":"Deng L (2012) The MNIST database of handwritten digit images for machine learning research [best of the web]. IEEE Signal Process Mag 29(6):141\u2013142","journal-title":"IEEE Signal Process Mag"},{"key":"1194_CR21","unstructured":"Netzer Y, Wang T, Coates A, Bissacco A, Wu B, Ng AY (2011) Reading digits in natural images with unsupervised feature learning. In: NIPS workshop on deep learning and unsupervised feature learning 2011, [Online]. http:\/\/ufldl.stanford.edu\/housenumbers\/nips2011_housenumbers.pdf"},{"key":"1194_CR22","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: The IEEE international conference on computer vision (ICCV), pp 1026\u20131034","DOI":"10.1109\/ICCV.2015.123"},{"key":"1194_CR23","unstructured":"Lin M, Chen Q, Yan S (2014) Network in network. In: International conference on learning representations (ICLR)"},{"key":"1194_CR24","unstructured":"Larsson G, Maire M, Shakhnarovich G (2017) FractalNet: ultra-deep neural networks without residuals. In: International conference on learning representations (ICLR)"},{"key":"1194_CR25","doi-asserted-by":"crossref","unstructured":"Xie S, Girshick R, Dollar P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 5987\u20135995","DOI":"10.1109\/CVPR.2017.634"},{"key":"1194_CR26","doi-asserted-by":"crossref","unstructured":"Zagoruyko S, Komodakis N (2016) Wide residual networks. In: Proceedings of the British Machine Vision Conference (BMVC), pp 87.1\u201387.12","DOI":"10.5244\/C.30.87"},{"key":"1194_CR27","first-page":"45","volume":"107","author":"M Sandler","year":"2019","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2019) Grading of hepatocellular carcinoma using 3D SE-DenseNet in dynamic enhanced MR images. Comput Biol Med 107:45\u201347","journal-title":"Comput Biol Med"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-020-01194-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-020-01194-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-020-01194-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,4]],"date-time":"2021-09-04T23:06:18Z","timestamp":1630796778000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-020-01194-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,5]]},"references-count":27,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["1194"],"URL":"https:\/\/doi.org\/10.1007\/s13042-020-01194-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,5]]},"assertion":[{"value":"5 March 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 August 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}