{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:38:35Z","timestamp":1783010315638,"version":"3.54.5"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,7,23]],"date-time":"2022-07-23T00:00:00Z","timestamp":1658534400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,7,23]],"date-time":"2022-07-23T00:00:00Z","timestamp":1658534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 62172047 and No. 61802020"],"award-info":[{"award-number":["No. 62172047 and No. 61802020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 62172047 and No. 61802020"],"award-info":[{"award-number":["No. 62172047 and No. 61802020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 62172047 and No. 61802020"],"award-info":[{"award-number":["No. 62172047 and No. 61802020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 62172047 and No. 61802020"],"award-info":[{"award-number":["No. 62172047 and No. 61802020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 62172047 and No. 61802020"],"award-info":[{"award-number":["No. 62172047 and No. 61802020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 62172047 and No. 61802020"],"award-info":[{"award-number":["No. 62172047 and No. 61802020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Cervical cancer cell detection is an essential means of cervical cancer screening. However, for thin-prep cytology test (TCT)-based images, the detection accuracies of traditional computer-aided detection algorithms are typically low due to the overlapping of cells with blurred cytoplasmic boundaries. Some typical deep learning-based detection methods, e.g., ResNets and Inception-V3, are not always efficient for cervical images due to the differences between cervical cancer cell images and natural images. As a result, these traditional networks are difficult to directly apply to the clinical practice of cervical cancer screening.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Method<\/jats:title>\n                <jats:p>We propose a cervical cancer cell detection network (3cDe-Net) based on an improved backbone network and multiscale feature fusion; the proposed network consists of the backbone network and a detection head. In the backbone network, a dilated convolution and a group convolution are introduced to improve the resolution and expression ability of the model. In the detection head, multiscale features are obtained based on a feature pyramid fusion network to ensure the accurate capture of small cells; then, based on the Faster region-based convolutional neural network (R-CNN), adaptive cervical cancer cell anchors are generated via unsupervised clustering. Furthermore, a new balanced L1-based loss function is defined, which reduces the unbalanced sample contribution loss.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Result<\/jats:title>\n                <jats:p>Baselines including ResNet-50, ResNet-101, Inception-v3, ResNet-152 and the feature concatenation network are used on two different datasets (the Data-T and Herlev datasets), and the final quantitative results show the effectiveness of the proposed dilated convolution ResNet (DC-ResNet) backbone network. Furthermore, experiments conducted on both datasets show that the proposed 3cDe-Net, based on the optimal anchors, the defined new loss function, and DC-ResNet, outperforms existing methods and achieves a mean average precision (mAP) of 50.4%. By performing a horizontal comparison of the cells on an image, the category and location information of cancer cells can be obtained concurrently.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The proposed 3cDe-Net can detect cancer cells and their locations on multicell pictures. The model directly processes and analyses samples at the picture level rather than at the cellular level, which is more efficient. In clinical settings, the mechanical workloads of doctors can be reduced, and their focus can be placed on higher-level review work.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-022-00852-z","type":"journal-article","created":{"date-parts":[[2022,7,23]],"date-time":"2022-07-23T10:03:17Z","timestamp":1658570597000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["3cDe-Net: a cervical cancer cell detection network based on an improved backbone network and multiscale feature fusion"],"prefix":"10.1186","volume":"22","author":[{"given":"Wei","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Xuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan-Song","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shi-Feng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan-Hua","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,7,23]]},"reference":[{"issue":"6","key":"852_CR1","first-page":"394","volume":"68","author":"F Bray","year":"2018","unstructured":"Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J Clin. 2018;68(6):394\u2013424.","journal-title":"CA: Cancer J Clin"},{"key":"852_CR2","unstructured":"Kurman RJ. The Bethesda system for reporting cervical\/vaginal cytologic diagnoses: definitions, criteria, and explanatory notes for terminology and specimen adequacy. Springer, Berlin; 2012."},{"key":"852_CR3","doi-asserted-by":"publisher","first-page":"2243","DOI":"10.1007\/s10489-021-02393-4","volume":"52","author":"E Jangam","year":"2022","unstructured":"Jangam E, Barreto AAD, Annavarapu CSR. Automatic detection of COVID-19 from chest CT scan and chest X-rays images using deep learning, transfer learning and stacking. Appl Intell. 2022;52:2243\u201359.","journal-title":"Appl Intell"},{"issue":"2","key":"852_CR4","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1002\/cncy.20067","volume":"118","author":"DJ Chute","year":"2010","unstructured":"Chute DJ, Lim H, Kong CS. BD focalpoint slide profiler performance with atypical glandular cells on SurePath Papanicolaou smears. Cancer Cytopathol. 2010;118(2):68\u201374.","journal-title":"Cancer Cytopathol"},{"key":"852_CR5","doi-asserted-by":"crossref","unstructured":"Bengtsson E, Malm P. Screening for cervical cancer using automated analysis of PAP-smears. Comput Math Methods Med. 2014.","DOI":"10.1155\/2014\/842037"},{"key":"852_CR6","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.cmpb.2018.05.034","volume":"164","author":"W William","year":"2018","unstructured":"William W, Ware A, Basaza-Ejiri AH, et al. A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images. Comput Methods Programs Biomed. 2018;164:15\u201322.","journal-title":"Comput Methods Programs Biomed"},{"key":"852_CR7","doi-asserted-by":"crossref","unstructured":"Akram SU, Kannala J, Eklund L, et al. Cell segmentation proposal network for microscopy image analysis. Deep learning and data labeling for medical applications. Springer, Cham, pp. 21\u201329; 2016.","DOI":"10.1007\/978-3-319-46976-8_3"},{"key":"852_CR8","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.bspc.2018.09.008","volume":"48","author":"P Wang","year":"2019","unstructured":"Wang P, Wang L, Li Y, et al. Automatic cell nuclei segmentation and classification of cervical Pap smear images. Biomed Signal Process Control. 2019;48:93\u2013103.","journal-title":"Biomed Signal Process Control"},{"key":"852_CR9","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1016\/j.future.2019.09.015","volume":"102","author":"A Ghoneim","year":"2020","unstructured":"Ghoneim A, Muhammad G, Hossain MS. Cervical cancer classification using convolutional neural networks and extreme learning machines. Futur Gener Comput Syst. 2020;102:643\u20139.","journal-title":"Futur Gener Comput Syst"},{"key":"852_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02781-w","author":"M Ghasemi","year":"2021","unstructured":"Ghasemi M, Kelarestaghi M, Eshghi F, et al. D3FC: deep feature-extractor discriminative dictionary-learning fuzzy classifier for medical imaging. Appl Intell. 2021. https:\/\/doi.org\/10.1007\/s10489-021-02781-w.","journal-title":"Appl Intell"},{"key":"852_CR11","unstructured":"Xu M.Q., Zeng W.X., Sun Y.H., et al. Cervical cytology intelligent diagnosis based on object detection technology. In: Processings of 1st conference on medical imaging with deep learning (MIDL), 2018, Amsterdam, The Netherlands."},{"issue":"2","key":"852_CR12","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1016\/j.bbe.2020.01.016","volume":"40","author":"Y Xiang","year":"2020","unstructured":"Xiang Y, Sun WX, Pan CL, et al. A novel automation-assisted cervical cancer reading method based on convolutional neural network. Biocybern Biomed Eng. 2020;40(2):611\u201323.","journal-title":"Biocybern Biomed Eng"},{"key":"852_CR13","unstructured":"Zhuang Z. Recognition of cervical cancer cells based on improved ResNet network. Beijing Jiaotong University; 2019."},{"issue":"2","key":"852_CR14","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s11263-019-01247-4","volume":"128","author":"L Liu","year":"2020","unstructured":"Liu L, Ouyang W, Wang X, et al. Deep learning for generic object detection: a survey. Int J Comput Vision. 2020;128(2):261\u2013318.","journal-title":"Int J Comput Vision"},{"issue":"6","key":"852_CR15","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, et al. Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell. 2017;39(6):1137\u201349.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"852_CR16","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition; 2014, arXiv preprint arXiv:1409.1556."},{"key":"852_CR17","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In:Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 2016, Los Alamitos, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"852_CR18","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Der Maaten LV, et al. Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 2017,Honolulu, HI, USA","DOI":"10.1109\/CVPR.2017.243"},{"key":"852_CR19","doi-asserted-by":"crossref","unstructured":"Li Z, Peng C, Yu G, et al. DetNet: design backbone for object detection. In: European conference on computer vision, 2018, Munich, Germany.","DOI":"10.1007\/978-3-030-01240-3_21"},{"key":"852_CR20","doi-asserted-by":"crossref","unstructured":"Geiger A, Lenz P, Urtasun R. Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR); 2012.","DOI":"10.1109\/CVPR.2012.6248074"},{"issue":"1","key":"852_CR21","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/s11517-020-02290-x","volume":"59","author":"M To\u011fa\u00e7ar","year":"2021","unstructured":"To\u011fa\u00e7ar M, Ergen B, C\u00f6mert Z. Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks. Med Biol Eng Compu. 2021;59(1):57\u201370.","journal-title":"Med Biol Eng Compu"},{"issue":"1","key":"852_CR22","first-page":"109","volume":"31","author":"M To\u011fa\u00e7ar","year":"2019","unstructured":"To\u011fa\u00e7ar M, Ergen B. Biyomedikal G\u00f6r\u00fcnt\u00fclerde Derin \u00d6\u011frenme ile Mevcut Y\u00f6ntemlerin K\u0131yaslanmas\u0131. F\u0131rat \u00dcniversitesi M\u00fchendislik Bilimleri Dergisi. 2019;31(1):109\u201321.","journal-title":"F\u0131rat \u00dcniversitesi M\u00fchendislik Bilimleri Dergisi"},{"key":"852_CR23","doi-asserted-by":"crossref","unstructured":"Xie S, Girshick R, Doll\u00e1r P, et al. Aggregated residual transformations for deep neural networks. In:Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 1492\u20131500; 2017.","DOI":"10.1109\/CVPR.2017.634"},{"key":"852_CR24","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A.: YOLO9000: better, faster, stronger. In:Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 2017, Los Alamitos, CA: IEEE Computer Society Press.","DOI":"10.1109\/CVPR.2017.690"},{"key":"852_CR25","doi-asserted-by":"crossref","unstructured":"Pang J, Chen K, Shi J, et al. Libra r-cnn: towards balanced learning for object detection. In:Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 2019,Los Alamitos, CA: IEEE Computer Society Press.","DOI":"10.1109\/CVPR.2019.00091"},{"key":"852_CR26","doi-asserted-by":"crossref","unstructured":"Nguyen LD, Lin D, Lin Z, et al. Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation. In: IEEE international symposium on circuits and systems, 2018, Piscataway.","DOI":"10.1109\/ISCAS.2018.8351550"},{"key":"852_CR27","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision. In:Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 2016; Los Alamitos, USA.","DOI":"10.1109\/CVPR.2016.308"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-022-00852-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-022-00852-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-022-00852-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,23]],"date-time":"2022-07-23T10:07:34Z","timestamp":1658570854000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-022-00852-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,23]]},"references-count":27,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["852"],"URL":"https:\/\/doi.org\/10.1186\/s12880-022-00852-z","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,23]]},"assertion":[{"value":"24 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 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":"We confirm that all methods were carried out in accordance with relevant guidelines and regulations.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that there is no conflict of interest regarding the publication of this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"130"}}