{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:03:08Z","timestamp":1774540988151,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976123"],"award-info":[{"award-number":["61976123"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s11517-023-02957-1","type":"journal-article","created":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T02:01:37Z","timestamp":1699581697000},"page":"563-573","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["DBPNDNet: dual-branch networks using 3DCNN toward pulmonary nodule detection"],"prefix":"10.1007","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4249-2264","authenticated-orcid":false,"given":"Muwei","family":"Jian","sequence":"first","affiliation":[]},{"given":"Haodong","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Linsong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Benzheng","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,10]]},"reference":[{"key":"2957_CR1","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1007\/s00366-020-01076-x","volume":"38","author":"J Zhang","year":"2022","unstructured":"Zhang J, Sun Y, Li G, Wang Y, Sun J, Li J (2022) Machine-learning-assisted shear strength prediction of reinforced concrete beams with and without stirrups. Eng Comput 38:1293\u20131307","journal-title":"Eng Comput"},{"key":"2957_CR2","doi-asserted-by":"publisher","unstructured":"Mai HT, Lieu QX, Kang J, Lee J (2022) A novel deep unsupervised learning-based framework for optimization of truss structures. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-022-01636-3","DOI":"10.1007\/s00366-022-01636-3"},{"key":"2957_CR3","doi-asserted-by":"publisher","unstructured":"Lee SY, Park C-S, Park K, Lee HJ, Lee S (2022) A Physics-informed and data-driven deep learning approach for wave propagation and its scattering characteristics. Eng Comput. https:\/\/doi.org\/10.1007\/s00366-022-01640-7","DOI":"10.1007\/s00366-022-01640-7"},{"key":"2957_CR4","unstructured":"Shoeibi A, Khodatars M, Alizadehsani R et al (2020) Automated detection and forecasting of covid- 19 using deep learning techniques: a review. 47(11):2533\u20132548. arXiv preprint arXiv:2007.10785"},{"key":"2957_CR5","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1007\/s00366-020-01042-7","volume":"38","author":"AF Ba","year":"2022","unstructured":"Ba AF, Huang H, Wang M, Ye X, Gu Z, Chen H, Cai X (2022) Levy-based antlion-inspired optimizers with orthogonal learning scheme. Eng Comput 38:397\u2013418","journal-title":"Eng Comput"},{"key":"2957_CR6","doi-asserted-by":"crossref","unstructured":"Liu W, Dragomir A, Dumitru E, Christian S, Scott R et al (2016) Ssd: single shot multibox detector, in Eur. Conf. Comput. Vis. Springer, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"2957_CR7","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In: IEEE Conf Comput Vis Pattern Recog, pp 7263\u20137271","DOI":"10.1109\/CVPR.2017.690"},{"key":"2957_CR8","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: IEEE Conf. Comput. Vis. Pattern Recog, pp 779\u2013788","DOI":"10.1109\/CVPR.2016.91"},{"issue":"7","key":"2957_CR9","doi-asserted-by":"publisher","first-page":"1558","DOI":"10.1109\/TBME.2016.2613502","volume":"64","author":"Q Dou","year":"2016","unstructured":"Dou Q, Chen H, Yu L, Qin J, Heng P-A (2016) Multilevel contextual 3-d cnns for false positive reduction in pulmonary nodule detection. IEEE Trans Biomed Eng 64(7):1558\u20131567","journal-title":"IEEE Trans Biomed Eng"},{"key":"2957_CR10","doi-asserted-by":"crossref","unstructured":"Li Y, Fan Y (2020) Deepseed: 3d squeeze-and-excitation encoder-decoder convolutional neural networks for pulmonary nodule detection. IEEE Int Symp Biomed Imaging\u00a0(ISBI) 2020:1866\u20131869","DOI":"10.1109\/ISBI45749.2020.9098317"},{"key":"2957_CR11","doi-asserted-by":"crossref","unstructured":"Yan X, Pang J, Qi H, Zhu Y, Bai C, Geng X, Liu M, Terzopoulos D, Ding X (2016) Classification of lung nodule malignancy risk on computed tomography images using convolutional neural network: a comparison between 2d and 3d strategies, in Asian Conf. Comput. Vis. Springer, pp 91\u2013101","DOI":"10.1007\/978-3-319-54526-4_7"},{"key":"2957_CR12","doi-asserted-by":"crossref","unstructured":"Tang H, Zhang C, Xie X (2019) Nodulenet: Decoupled false positive reduction for pulmonary nodule detection and segmentation, in International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp 266\u2013274","DOI":"10.1007\/978-3-030-32226-7_30"},{"issue":"1","key":"2957_CR13","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s11517-021-02462-3","volume":"60","author":"R Manickavasagam","year":"2022","unstructured":"Manickavasagam R, Selvan S, Selvan M (2022) CAD system for lung nodule detection using deep learning with CNN. Med Biol Eng Compu 60(1):221\u2013228","journal-title":"Med Biol Eng Compu"},{"key":"2957_CR14","doi-asserted-by":"crossref","unstructured":"Mahmood SA, Ahmed HA (2022) An improved CNN-based architecture for automatic lung nodule classification. Med Biol Eng Comput\u00a060(7):1\u201310","DOI":"10.1007\/s11517-022-02578-0"},{"key":"2957_CR15","doi-asserted-by":"crossref","unstructured":"Sherwani MK, Marzullo A, De Momi E, Calimeri F (2022) Lesion segmentation in lung CT scans using unsupervised adversarial learning. Med Biol Eng Comput\u00a060(11):1\u201313","DOI":"10.1007\/s11517-022-02651-8"},{"issue":"1","key":"2957_CR16","first-page":"73","volume":"1","author":"N Yu","year":"2022","unstructured":"Yu N, Yang R, Huang M (2022) Deep common spatial pattern based motor imagery classification with improved objective function. Int J Netw Dyn Intell 1(1):73\u201384","journal-title":"Int J Netw Dyn Intell"},{"issue":"1","key":"2957_CR17","first-page":"111","volume":"1","author":"Q Zhang","year":"2022","unstructured":"Zhang Q, Zhou Y (2022) Recent advances in non-Gaussian stochastic systems control theory and its applications. Int J Netw Dyn Intell 1(1):111\u2013119","journal-title":"Int J Netw Dyn Intell"},{"issue":"1","key":"2957_CR18","first-page":"85","volume":"1","author":"X Wang","year":"2022","unstructured":"Wang X, Sun Y, Ding D (2022) Adaptive dynamic programming for networked control systems under communication constraints: a survey of trends and techniques. Int J Netw Dyn Intell 1(1):85\u201398","journal-title":"Int J Netw Dyn Intell"},{"key":"2957_CR19","doi-asserted-by":"crossref","unstructured":"Akhavanallaf A, Shiri I, Arabi H, Zaidi H (2020) Whole-body voxel-based internal dosimetry using deep learning. European J Nucl Med Mol Imaging\u00a048:670\u2013682","DOI":"10.1007\/s00259-020-05013-4"},{"key":"2957_CR20","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.ejmp.2021.03.008","volume":"83","author":"H Arabi","year":"2021","unstructured":"Arabi H, Akhavanallaf A et al (2021) The promise of artificial intelligence and deep learning in PET and SPECT imaging. Phys Med 83:122\u2013137","journal-title":"Phys Med"},{"issue":"1","key":"2957_CR21","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s41824-020-00086-8","volume":"4","author":"H Arabi","year":"2021","unstructured":"Arabi H, Zaidi H (2021) Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy. European J Hybrid Imaging 4(1):17","journal-title":"European J Hybrid Imaging"},{"key":"2957_CR22","doi-asserted-by":"crossref","unstructured":"Shiri I, Arabi H, Geramifar P et al (2020) Deep-JASC: joint attenuation and scatter correction in whole-body 18 F-FDG PET using a deep residual network. European J Nucl Med Mol Imaging\u00a047:2533\u20132548","DOI":"10.1007\/s00259-020-04852-5"},{"issue":"3","key":"2957_CR23","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1016\/j.media.2010.02.004","volume":"14","author":"T Messay","year":"2010","unstructured":"Messay T, Hardie RC, Rogers SK (2010) A new computationally efficient cad system for pulmonary nodule detection in ct imagery. Med Image Anal 14(3):390\u2013406","journal-title":"Med Image Anal"},{"key":"2957_CR24","doi-asserted-by":"crossref","unstructured":"Duggan N, Bae E, Shen S, Hsu W, Bui A, Jones E, Glavin M, Vese L (2015) A technique for lung nodule candidate detection in ct using global minimization methods. In: Int. Worksh. Energy Minimization Methods in Comput. Vis. Pattern Recog. Springer, pp 478\u2013491","DOI":"10.1007\/978-3-319-14612-6_35"},{"issue":"2","key":"2957_CR25","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.media.2013.12.001","volume":"18","author":"C Jacobs","year":"2014","unstructured":"Jacobs C, Rikxoort EM, Twellmann T et al (2014) Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images. Med Image Anal 18(2):374\u2013384","journal-title":"Med Image Anal"},{"key":"2957_CR26","unstructured":"Fu C, Liu W, Ranga A, Tyagi A (2017) Dssd: deconvolutional single shot detector. arXiv preprint arXiv:1701.06659"},{"key":"2957_CR27","doi-asserted-by":"crossref","unstructured":"Lin T, Goyal P, Girshick R, He K (2017) Focal loss for dense object detection. Int Conf Comput Vis 2017:2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"issue":"5","key":"2957_CR28","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1109\/TMI.2016.2536809","volume":"35","author":"A Setio","year":"2016","unstructured":"Setio A, Ciompi F, Litjens G, Gerke P, Jacobs C et al (2016) Pulmonary nodule detection in ct images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging 35(5):1160\u20131169","journal-title":"IEEE Trans Med Imaging"},{"key":"2957_CR29","doi-asserted-by":"crossref","unstructured":"Liao F, Liang M, Li Z, Hu X, Song S (2019) Evaluate the malignancy of pulmonary nodules using the 3-d deep leaky noisy-or network. IEEE Tran Neural Netw Learn Syst\u00a030(11):3484\u20133495","DOI":"10.1109\/TNNLS.2019.2892409"},{"key":"2957_CR30","doi-asserted-by":"publisher","first-page":"102287","DOI":"10.1016\/j.media.2021.102287","volume":"75","author":"X Luo","year":"2022","unstructured":"Luo X, Song T et al (2022) Scpm-net: an anchor-free 3d lung nodule detection network using sphere representation and center points matching. Med Image Anal 75:102287","journal-title":"Med Image Anal"},{"issue":"7","key":"2957_CR31","doi-asserted-by":"publisher","first-page":"3741","DOI":"10.1002\/mp.14915","volume":"48","author":"MM Farhangi","year":"2021","unstructured":"Farhangi MM, Sahiner B, Petrick N, Pezehsk A (2021) Automatic lung nodule detection in thoracic CT scans using dilated slice-wise convolutions. Med Phys 48(7):3741\u20133751","journal-title":"Med Phys"},{"key":"2957_CR32","doi-asserted-by":"crossref","unstructured":"Song T et al (2020) CPM-Net: A 3D center-points matching network for pulmonary nodule detection in CT scans. In: International Conference on MICCAI. Springer, vol 12266, pp 550\u2013559","DOI":"10.1007\/978-3-030-59725-2_53"},{"issue":"6","key":"2957_CR33","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2020","unstructured":"Zhou Z et al (2020) UNet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging 39(6):1856\u20131867","journal-title":"IEEE Trans Med Imaging"},{"key":"2957_CR34","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi SA (2016) V-Net: fully convolutional neural networks for volumetric medical image segmentation. Proceedings of 2016 Fourth International Conference on 3D Vision (3DV), pp 565\u2013571","DOI":"10.1109\/3DV.2016.79"},{"key":"2957_CR35","unstructured":"Oktay O et al (2018) Attention U-Net: learning where to look for the pancreas. Comput Vision Pattern Recog 3"},{"key":"2957_CR36","doi-asserted-by":"crossref","unstructured":"Kamal U et al (2020) Lung cancer tumor region segmentation using recurrent 3D-DenseUNet. MICCAI 2020 Thoracic Image Analysis (TIA) Workshop 2020, 12502:36\u201347","DOI":"10.1007\/978-3-030-62469-9_4"},{"key":"2957_CR37","doi-asserted-by":"publisher","first-page":"102156","DOI":"10.1016\/j.media.2021.102156","volume":"73","author":"X Liang","year":"2021","unstructured":"Liang X et al (2021) Incorporating the hybrid deformable model for improving the performance of abdominal CT segmentation via multi-scale feature fusion network. Med Image Anal 73:102156","journal-title":"Med Image Anal"},{"key":"2957_CR38","doi-asserted-by":"publisher","first-page":"102152","DOI":"10.1016\/j.media.2021.102152","volume":"73","author":"C Shi","year":"2021","unstructured":"Shi C et al (2021) Multi-slice low-rank tensor decomposition based multi-atlas segmentation: application to automatic pathological liver CT segmentation. Med Image Anal 73:102152","journal-title":"Med Image Anal"},{"key":"2957_CR39","doi-asserted-by":"publisher","first-page":"114219","DOI":"10.1016\/j.eswa.2020.114219","volume":"168","author":"MW Jian","year":"2021","unstructured":"Jian MW, Wang J, Yu H, Wang GD, Meng XJ, Yang L, Dong JY, Yin YL (2021) Visual saliency detection by integrating spatial position prior of object with background cues. Exp Syst Appl 168:114219","journal-title":"Exp Syst Appl"},{"key":"2957_CR40","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1016\/j.ins.2021.08.069","volume":"576","author":"MW Jian","year":"2021","unstructured":"Jian MW, Wang JJ, Yu H, Wang GG (2021) Integrating object proposal with attention networks for video saliency detection. Inf Sci 576:819\u2013830","journal-title":"Inf Sci"},{"issue":"45","key":"2957_CR41","first-page":"33465","volume":"79","author":"MW Jian","year":"2020","unstructured":"Jian MW, Wang J, Dong JY, Cui CR, Nie XS, Yin YL (2020) Saliency detection using multiple low-level priors and a propagation mechanism. Multimed Tools Appl 79(45):33465\u201333482","journal-title":"Multimed Tools Appl"},{"key":"2957_CR42","doi-asserted-by":"crossref","unstructured":"Lu XW, Jian MW, Wang X, Yu H, Dong JY, Lam KM (2020) Visual saliency detection via combining center prior and U-Net.\u00a0Multimed Systs\u00a028(5):1689\u20131698","DOI":"10.1007\/s00530-022-00940-8"},{"key":"2957_CR43","doi-asserted-by":"crossref","unstructured":"Zhu W, Liu C, Fan W, Xie X (2018) Deeplung: deep 3d dual path nets for automated pulmonary nodule detection and classification. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, pp 673\u2013681","DOI":"10.1109\/WACV.2018.00079"},{"key":"2957_CR44","doi-asserted-by":"crossref","unstructured":"Ren S, He K, Girshick R,\u00a0Sun J\u00a0(2015) Faster r-cnn: towards real-time object detection with region proposal networks.\u00a0IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"2957_CR45","doi-asserted-by":"crossref","unstructured":"Mei J, Cheng MM, Xu G, Wan LR, Zhang H (2021) Sanet: a slice aware network for pulmonary nodule detection. IEEE Trans Pattern Anal Mach Intell pp 1\u201319","DOI":"10.1109\/TPAMI.2021.3065086"},{"issue":"2","key":"2957_CR46","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","volume":"38","author":"SG Armato","year":"2011","unstructured":"Armato SG, McLennan G et al (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","journal-title":"Med Phys"},{"key":"2957_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2017.06.015","volume":"42","author":"A Setio","year":"2017","unstructured":"Setio A et al (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","journal-title":"Med Image Anal"},{"key":"2957_CR48","doi-asserted-by":"crossref","unstructured":"Zuo W, Zhou F, He Y (2020) An embedded multi-branch 3d convolution neural network for false positive reduction in lung nodule detection. J Digit Imaging 33:(4)","DOI":"10.1007\/s10278-020-00326-0"},{"key":"2957_CR49","first-page":"1","volume":"99","author":"H Yuan","year":"2021","unstructured":"Yuan H et al (2021) Pulmonary nodule detection using 3-d residual u-net oriented context-guided attention and multi-branch classification network. IEEE Access 99:1\u20131","journal-title":"IEEE Access"},{"issue":"5","key":"2957_CR50","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1109\/TMI.2019.2935553","volume":"39","author":"S Zheng","year":"2020","unstructured":"Zheng S et al (2020) Automatic pulmonary nodule detection in ct scans using convolutional neural networks based on maximum intensity projection. IEEE Trans Med Imaging 39(5):797\u2013805","journal-title":"IEEE Trans Med Imaging"},{"issue":"5","key":"2957_CR51","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1109\/TMI.2019.2947595","volume":"39","author":"O Ozdemir","year":"2020","unstructured":"Ozdemir O, Russell RL, Berlin AA (2020) A 3D probabilistic deep learning system for detection and diagnosis of lung cancer using low-dose CT scans. IEEE Trans Med Imaging 39(5):1419\u20131429","journal-title":"IEEE Trans Med Imaging"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02957-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-023-02957-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02957-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T06:10:23Z","timestamp":1705471823000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-023-02957-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,10]]},"references-count":51,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["2957"],"URL":"https:\/\/doi.org\/10.1007\/s11517-023-02957-1","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,10]]},"assertion":[{"value":"27 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 2023","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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors declared that they have no involving Human Participants and\/or Animals to this work.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving Human Participants and\/or Animals"}}]}}