{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:30:13Z","timestamp":1771065013066,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"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\/s11517-023-02947-3","type":"journal-article","created":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T22:03:15Z","timestamp":1698876195000},"page":"465-478","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Nuclei detection in breast histopathology images with iterative correction"],"prefix":"10.1007","volume":"62","author":[{"given":"Ziyi","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8185-0610","authenticated-orcid":false,"given":"Qiling","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,1]]},"reference":[{"key":"2947_CR1","doi-asserted-by":"publisher","first-page":"2318","DOI":"10.1016\/j.humpath.2012.03.027","volume":"43","author":"S Al-Janabi","year":"2012","unstructured":"Al-Janabi S, Huisman A, Willems SM, van Diest PJ (2012) Digital slide images for primary diagnostics in breast pathology: a feasibility study. Hum Pathol 43:2318\u20132325","journal-title":"Hum Pathol"},{"issue":"11","key":"2947_CR2","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1001\/jama.2015.1405","volume":"313","author":"JG Elmore","year":"2015","unstructured":"Elmore JG, Longton GM, Carney PA et al (2015) Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA - J Am Med Assoc 313(11):1122\u20131132","journal-title":"JAMA - J Am Med Assoc"},{"key":"2947_CR3","doi-asserted-by":"crossref","unstructured":"H. Viray, K. Li, T.A. Long., P. Vasalos, et al., A prospective, multi-institutional diagnostic trial to determine pathologist accuracy in estimation of percentage of malignant cells, Arch. Pathol. Lab. Med., vol.\u00a0137, no. 11, pp. 1545\u20131549, 2013.","DOI":"10.5858\/arpa.2012-0561-CP"},{"issue":"5","key":"2947_CR4","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1111\/j.1365-2559.1991.tb00229.x","volume":"19","author":"CW Elston","year":"1991","unstructured":"Elston CW, Ellis IO (1991) Pathological prognostic factors in breast cancer I The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology 19(5):403\u2013410","journal-title":"Histopathology"},{"issue":"2","key":"2947_CR5","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1109\/TMI.2014.2361481","volume":"34","author":"X Zhang","year":"2015","unstructured":"Zhang X, Liu W, Dundar M, Badve S, Zhang S (2015) Towards largescale histopathological image analysis: hashing-based image retrieval. IEEE Trans Med Imaging 34(2):496\u2013506","journal-title":"IEEE Trans Med Imaging"},{"key":"2947_CR6","doi-asserted-by":"crossref","unstructured":"W. Lu, S. Graham, M. Bilal, N. Rajpoot, and F. Minhas, Capturing cellular topology in multi-gigapixel pathology images, in Proc. IEEE Conf. Comput. Vis. Pattern Recog. Workshops, 2020, pp. 1049\u20131058.","DOI":"10.1109\/CVPRW50498.2020.00138"},{"key":"2947_CR7","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1109\/RBME.2013.2295804","volume":"7","author":"H Irshad","year":"2014","unstructured":"Irshad H, Veillard A, Roux L, Racoceanu D (2014) Methods for nuclei detection, segmentation, and classification in digital histopathology: a review\u2014current status and future potential. IEEE Rev Biomed Eng 7:97\u2013114","journal-title":"IEEE Rev Biomed Eng"},{"issue":"1","key":"2947_CR8","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.canlet.2015.11.018","volume":"380","author":"S Nawaz","year":"2016","unstructured":"Nawaz S, Yuan Y (2016) Computational pathology: exploring the spatial dimension of tumor ecology. Cancer Lett 380(1):296\u2013303","journal-title":"Cancer Lett"},{"key":"2947_CR9","unstructured":"S Ren, K He, R Girshick, J Sun (2015) Faster R-CNN: towards realtime object detection with region proposal networks, in Proc. Adv Neural Inf Process Syst (NIPS) 1: 91-99"},{"key":"2947_CR10","doi-asserted-by":"crossref","unstructured":"S. Yousefi and Y. Nie, Transfer learning from nucleus detection to classification in histopathology images, in Proc. IEEE Int. Symp. Biomed. Imaging (ISBI), 2019, pp. 957\u2013960.","DOI":"10.1101\/530113"},{"key":"2947_CR11","doi-asserted-by":"crossref","unstructured":"T. Mahmood, M. Arsalan, M. Owais, M.B. Lee, and K.R. Park (2020) Artificial intelligence-based mitosis detection in breast cancer histopathology images using faster R-CNN and Deep CNNs. J. Clin. Med 9 3.","DOI":"10.3390\/jcm9030749"},{"issue":"1","key":"2947_CR12","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1109\/TMI.2015.2458702","volume":"35","author":"J Xu","year":"2016","unstructured":"Xu J, Xiang L, Liu Q, Gilmore H, Wu J, Tang J, Madabhushi A (2016) Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imaging 35(1):119\u2013130","journal-title":"IEEE Trans Med Imaging"},{"key":"2947_CR13","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.compmedimag.2018.08.010","volume":"70","author":"H H\u00f6fener","year":"2018","unstructured":"H\u00f6fener H, Homeyer A, Weiss N, Molin J, Lundstr\u00f6m CF, Hahn HK (2018) Deep learning nuclei detection: a simple approach can deliver state-of-the-art results. Comput Med Imag Grap 70:43\u201352","journal-title":"Comput Med Imag Grap"},{"key":"2947_CR14","doi-asserted-by":"crossref","unstructured":"Brieu N, Schmidt G (2017) Learning size adaptive local maxima selection for robust nuclei detection in histopathology images. In: Proc IEEE Intl Symp Biomed Imaging, pp 937\u2013941","DOI":"10.1109\/ISBI.2017.7950670"},{"key":"2947_CR15","doi-asserted-by":"crossref","unstructured":"Chen T, Chefdhotel C (2014) Deep learning based automatic immune cell detection for immunohistochemistry images. In: Proc Intl Workshop Mach Learn Med Imaging, pp 17\u201324","DOI":"10.1007\/978-3-319-10581-9_3"},{"key":"2947_CR16","doi-asserted-by":"crossref","unstructured":"Bodla N, Singh B, Chellappa R, Davis LS (2017) Soft-NMS improving object detection with one line of code. In: Proc IEEE Intl Conf Computer Vision (ICCV), pp 5562\u20135570","DOI":"10.1109\/ICCV.2017.593"},{"key":"2947_CR17","doi-asserted-by":"crossref","unstructured":"Tychsen-Smith L, Petersson L (2018) Improving object localization with fitness NMS and bounded IoU loss. In: Proc. IEEE Conf Comput Vis Pattern Recog (CVPR), pp 6877\u20136885","DOI":"10.1109\/CVPR.2018.00719"},{"key":"2947_CR18","doi-asserted-by":"crossref","unstructured":"Oberweger M, Wohlhart P, Lepetit V (2015) Training a feedback loop for hand pose estimation. In: Proc IEEE Intl Conf Computer Vision (ICCV), pp 3316\u20133324","DOI":"10.1109\/ICCV.2015.379"},{"key":"2947_CR19","doi-asserted-by":"crossref","unstructured":"Carreira J, Agrawal P, Fragkiadaki K, Malik J (2016) Human pose estimation with iterative error feedback. In: Proc IEEE Conf Comput Vis Pattern Recog (CVPR), pp 4733\u20134742","DOI":"10.1109\/CVPR.2016.512"},{"issue":"1","key":"2947_CR20","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s11263-017-1004-z","volume":"125","author":"S Xie","year":"2017","unstructured":"Xie S, Tu Z (2017) Holistically-nested edge detection. Int J Comput Vis 125(1):3\u201318","journal-title":"Int J Comput Vis"},{"issue":"8","key":"2947_CR21","doi-asserted-by":"publisher","first-page":"1939","DOI":"10.1109\/TPAMI.2018.2878849","volume":"41","author":"Y Liu","year":"2019","unstructured":"Liu Y, Cheng M-M, Hu X, Bian J-W, Zhang L, Bai X, Tang J (2019) Richer convolutional features for edge detection. IEEE Trans Pattern Anal Mach Intell 41(8):1939\u20131946","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2947_CR22","doi-asserted-by":"crossref","unstructured":"Xie Y, Xing F, Kong X, Su H, Yang L (2015) Beyond classification: structured regression for robust cell detection using convolutional neural network. In: Proc Medical Image Computing and Computer-assisted Intervention (MICCAI), pp 358\u2013365","DOI":"10.1007\/978-3-319-24574-4_43"},{"issue":"2","key":"2947_CR23","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1109\/JBHI.2013.2277837","volume":"18","author":"C Lu","year":"2014","unstructured":"Lu C, Mandal M (2014) Toward automatic mitotic cell detection and segmentation in multispectral histopathological images. IEEE J Biomed Health Inform 18(2):594\u2013605","journal-title":"IEEE J Biomed Health Inform"},{"issue":"11","key":"2947_CR24","doi-asserted-by":"publisher","first-page":"4041","DOI":"10.1109\/TIP.2015.2460455","volume":"24","author":"A Paul","year":"2015","unstructured":"Paul A, Mukherjee DP (2015) Mitosis detection for invasive breast cancer grading in histopathological images. IEEE Trans Image Process 24(11):4041\u20134054","journal-title":"IEEE Trans Image Process"},{"issue":"5","key":"2947_CR25","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.1109\/TMI.2016.2525803","volume":"35","author":"K Sirinukunwattana","year":"2016","unstructured":"Sirinukunwattana K, Raza SEA, Tsang Y-W, Snead D, Cree I, Rajpoot N (2016) Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging 35(5):1196\u20131206","journal-title":"IEEE Trans Med Imaging"},{"key":"2947_CR26","doi-asserted-by":"crossref","unstructured":"Ciresan DC, Giusti A, Gambardella LM, Schmidhuber J (2013) Mitosis detection in breast cancer histology images with deep neural networks. In: Proc Medical Image Computing and Computer-assisted Intervention (MICCAI), pp 411\u2013418","DOI":"10.1007\/978-3-642-40763-5_51"},{"issue":"4","key":"2947_CR27","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640\u2013651","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2947_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2019.101563","volume":"58","author":"S Graham","year":"2019","unstructured":"Graham S, Vu QD, Raza SEA, Azam A, Tsang YW, Kwak JT, Rajpoot N (2019) Hover-net: simultaneous segmentation and classification of nuclei in multi-tissue histology images. Med. Image Anal 58:1\u201315","journal-title":"Med. Image Anal"},{"key":"2947_CR29","doi-asserted-by":"crossref","unstructured":"Chen H, Dou Q, Wang X, Qin J, Heng PA (2016) Mitosis detection in breast cancer histology images via deep cascaded networks. In: Proc AAAI Conf Artificial Intelligence, pp 1160\u20131166","DOI":"10.1609\/aaai.v30i1.10140"},{"key":"2947_CR30","doi-asserted-by":"crossref","unstructured":"Zhang J, Hu H, Chen S, Huang Y, Guan Q (2016) Cancer cells detection in phase-contrast microscopy images based on faster R-CNN. In: Proc Intl Symp Computational Intelligence and Design, pp 363\u2013367","DOI":"10.1109\/ISCID.2016.1090"},{"key":"2947_CR31","doi-asserted-by":"crossref","unstructured":"Yi J, Wu P, Hoeppner DJ, Metaxas D (2017) Fast neural cell detection using light-weight SSD neural network. In: Proc IEEE Conf Comput Vis Pattern Recog Workshops, pp 108\u2013112","DOI":"10.1109\/CVPRW.2017.119"},{"key":"2947_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2021.102142","volume":"72","author":"Y Sun","year":"2021","unstructured":"Sun Y, Huang X, Zhou H, Zhang Q (2021) SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images. Med Image Anal 72:1\u201311","journal-title":"Med Image Anal"},{"key":"2947_CR33","unstructured":"Vargas V, Koller J (2018) Semi-supervised generative adversarial network (SGAN) for nuclei detection on breast cancer histopathology images. [Online]. Available: https:\/\/github.com\/vmvargas\/GAN-for-Nuclei-Detection. Accessed 7 May 2021"},{"key":"2947_CR34","unstructured":"Ciresan D, Giusti A, Gambardella LM, Schmidhuber J (2012) Deep neural networks segment neuronal membranes in electron microscopy images. In: Proc Adv Neural Inf Process Syst (NIPS), pp 2843\u20132851"},{"key":"2947_CR35","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY, Kweon IS (2018) CBAM: convolutional block attention module. In: Proc European Conf Computer Vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"issue":"8","key":"2947_CR36","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","volume":"42","author":"J Hu","year":"2020","unstructured":"Hu J, Shen L, Albanie S, Sun G, Wu E (2020) Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell 42(8):2011\u20132023","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2947_CR37","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: Proc IEEE Intl Conf Computer Vision (ICCV), pp 1026\u20131034","DOI":"10.1109\/ICCV.2015.123"},{"key":"2947_CR38","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proc Intl Conf Machine Learning (ICML), pp 448\u2013456"},{"key":"2947_CR39","doi-asserted-by":"crossref","unstructured":"Song G, Liu Y, Wang X (2020) Revisiting the sibling head in object detector. In: Proc IEEE Conf Comput Vis Pattern Recog (CVPR), pp 11560\u201311569","DOI":"10.1109\/CVPR42600.2020.01158"},{"key":"2947_CR40","doi-asserted-by":"crossref","unstructured":"Wu Y, Chen Y, Yuan L, Liu Z, Wang L, Li H, Fu Y (2020) Rethinking classification and localization for object detection. In: Proc IEEE Conf Comput Vis Pattern Recog (CVPR), pp 10183\u201310192","DOI":"10.1109\/CVPR42600.2020.01020"},{"key":"2947_CR41","doi-asserted-by":"publisher","unstructured":"Wang CY, Bochkovskiy A, Liao HYM (2022) YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. https:\/\/doi.org\/10.48550\/arXiv.2207.02696","DOI":"10.48550\/arXiv.2207.02696"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02947-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-023-02947-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02947-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T06:08:57Z","timestamp":1705471737000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-023-02947-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,1]]},"references-count":41,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["2947"],"URL":"https:\/\/doi.org\/10.1007\/s11517-023-02947-3","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,1]]},"assertion":[{"value":"22 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 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":"Not applicable. All analyses were based on public dataset; thus no ethical approval and patient consent are required.","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 no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving human participants and\/or animals"}}]}}