{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T09:08:13Z","timestamp":1790500093012,"version":"4.1.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T00:00:00Z","timestamp":1616112000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T00:00:00Z","timestamp":1616112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s10278-021-00442-5","type":"journal-article","created":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T20:10:31Z","timestamp":1616184631000},"page":"667-677","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["HER2 Molecular Marker Scoring Using Transfer Learning and Decision Level Fusion"],"prefix":"10.1007","volume":"34","author":[{"given":"Suman","family":"Tewary","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4719-2578","authenticated-orcid":false,"given":"Sudipta","family":"Mukhopadhyay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,3,19]]},"reference":[{"key":"442_CR1","doi-asserted-by":"crossref","unstructured":"Ma J, Jemal A: Breast cancer statistics: Springer, 2013","DOI":"10.1007\/978-1-4614-5647-6_1"},{"key":"442_CR2","doi-asserted-by":"crossref","unstructured":"Mosquera-Lopez C, Agaian S, Velez-Hoyos A, Thompson I, et al: Computer-aided prostate cancer diagnosis from digitized histopathology: a review on texture-based systems. IEEE reviews in biomedical engineering 8:98-113, 2014","DOI":"10.1109\/RBME.2014.2340401"},{"key":"442_CR3","doi-asserted-by":"crossref","unstructured":"Joensuu K, Leidenius M, Kero M, Andersson LC, Horwitz KB, Heikkil\u00e4 P, et al: ER, PR, HER2, Ki-67 and CK5 in early and late relapsing breast cancer--reduced CK5 expression in metastases. Breast cancer: basic and clinical research 7:23, 2013","DOI":"10.4137\/BCBCR.S10701"},{"key":"442_CR4","unstructured":"Wolff AC, et al: Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology\/College of American Pathologists clinical practice guideline update. Archives of Pathology and Laboratory Medicine 138:241-256, 2014"},{"key":"442_CR5","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1136\/jclinpath-2014-202571","volume":"68","author":"EA Rakha","year":"2015","unstructured":"Rakha EA, et al: Updated UK Recommendations for HER2 assessment in breast cancer. Journal of clinical pathology 68:93-99, 2015","journal-title":"Journal of clinical pathology"},{"key":"442_CR6","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1111\/pin.12407","volume":"66","author":"H Nitta","year":"2016","unstructured":"Nitta H, et al: The assessment of HER2 status in breast cancer: the past, the present, and the future. Pathology international 66:313-324, 2016","journal-title":"Pathology international"},{"key":"442_CR7","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1111\/his.13333","volume":"72","author":"T Qaiser","year":"2018","unstructured":"Qaiser T, et al: Her 2 challenge contest: a detailed assessment of automated her 2 scoring algorithms in whole slide images of breast cancer tissues. Histopathology 72:227-238, 2018","journal-title":"Histopathology"},{"key":"442_CR8","doi-asserted-by":"crossref","unstructured":"Cordeiro CQ, Ioshii SO, Alves JH, Oliveira LF et al: An Automatic Patch-based Approach for HER-2 Scoring in Immunohistochemical Breast Cancer Images Using Color Features. arXiv preprint https:\/\/arxiv.org\/1805.05392, 2018","DOI":"10.5753\/sbcas.2018.3685"},{"key":"442_CR9","doi-asserted-by":"publisher","first-page":"610","DOI":"10.5858\/arpa.2011-0371-OA","volume":"136","author":"J Jeung","year":"2012","unstructured":"Jeung J, Patel R, Vila L, Wakefield D, Liu C et al: Quantitation of HER2\/neu expression in primary gastroesophageal adenocarcinomas using conventional light microscopy and quantitative image analysis. Archives of pathology & laboratory medicine 136:610-617, 2012","journal-title":"Archives of pathology & laboratory medicine"},{"key":"442_CR10","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s10549-011-1514-2","volume":"132","author":"A Br\u00fcgmann","year":"2012","unstructured":"Br\u00fcgmann A, et al: Digital image analysis of membrane connectivity is a robust measure of HER2 immunostains. Breast cancer research and treatment 132:41-49, 2012","journal-title":"Breast cancer research and treatment"},{"key":"442_CR11","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1111\/j.1365-2559.2010.03577.x","volume":"57","author":"L Dobson","year":"2010","unstructured":"Dobson L, et al: Image analysis as an adjunct to manual HER\u20102 immunohistochemical review: a diagnostic tool to standardize interpretation. Histopathology 57:27-38, 2010","journal-title":"Histopathology"},{"key":"442_CR12","first-page":"291","volume":"23","author":"AC Ruifrok","year":"2001","unstructured":"Ruifrok AC, Johnston DA: Quantification of histochemical staining by color deconvolution. Analytical and quantitative cytology and histology 23:291-299, 2001","journal-title":"Analytical and quantitative cytology and histology"},{"key":"442_CR13","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1111\/j.1365-2559.2011.04142.x","volume":"60","author":"VJ Tuominen","year":"2012","unstructured":"Tuominen VJ, Tolonen TT, Isola J et al: ImmunoMembrane: a publicly available web application for digital image analysis of HER2 immunohistochemistry. Histopathology 60:758-767, 2012","journal-title":"Histopathology"},{"key":"442_CR14","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.compbiomed.2014.03.001","volume":"49","author":"M Tabakov","year":"2014","unstructured":"Tabakov M, Kozak P: Segmentation of histopathology HER2\/neu images with fuzzy decision tree and Takagi\u2013Sugeno reasoning. Computers in biology and medicine 49:19-29, 2014","journal-title":"Computers in biology and medicine"},{"key":"442_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1746-1596-2-8","volume":"2","author":"N-A Pham","year":"2007","unstructured":"Pham N-A, et al: Quantitative image analysis of immunohistochemical stains using a CMYK color model. Diagnostic pathology 2:1, 2007","journal-title":"Diagnostic pathology"},{"key":"442_CR16","unstructured":"Wdowiak M, Markiewicz T, Osowski S, Swiderska Z, Patera J, Kozlowski W, et al: Hourglass shapes in rank grey-level hit-or-miss transform for membrane segmentation in HER2\/neu images. Proc. International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing: City"},{"key":"442_CR17","doi-asserted-by":"crossref","unstructured":"Gavrielides MA, Masmoudi H, Petrick N, Myers KJ, Hewitt SM, et al: Automated evaluation of HER-2\/neu immunohistochemical expression in breast cancer using digital microscopy. Proc. 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro: City","DOI":"10.1109\/ISBI.2008.4541119"},{"key":"442_CR18","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neucom.2016.05.084","volume":"229","author":"T Wan","year":"2017","unstructured":"Wan T, Cao J, Chen J, Qin Z, et al: Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features. Neurocomputing 229:34-44, 2017","journal-title":"Neurocomputing"},{"key":"442_CR19","doi-asserted-by":"publisher","first-page":"35","DOI":"10.3390\/jimaging5030035","volume":"5","author":"R Mukundan","year":"2019","unstructured":"Mukundan R: Analysis of Image Feature Characteristics for Automated Scoring of HER2 in Histology Slides. Journal of Imaging 5:35, 2019","journal-title":"Journal of Imaging"},{"key":"442_CR20","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1111\/jmi.12955","volume":"281","author":"S Tewary","year":"2021","unstructured":"Tewary S, Arun I, Ahmed R, Chatterjee S, Mukhopadhyay S, et al: AutoIHC\u2010Analyzer: computer\u2010assisted microscopy for automated membrane extraction\/scoring in HER2 molecular markers. Journal of Microscopy 281:87-96, 2021","journal-title":"Journal of Microscopy"},{"key":"442_CR21","first-page":"436","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G, et al: Deep learning. nature 521:436-444, 2015","journal-title":"Deep learning. nature"},{"key":"442_CR22","doi-asserted-by":"publisher","first-page":"1625","DOI":"10.1109\/JBHI.2017.2691738","volume":"21","author":"Y Huang","year":"2017","unstructured":"Huang Y, Zheng H, Liu C, Ding X, Rohde GK, et al: Epithelium-stroma classification via convolutional neural networks and unsupervised domain adaptation in histopathological images. IEEE journal of biomedical and health informatics 21:1625-1632, 2017","journal-title":"IEEE journal of biomedical and health informatics"},{"key":"442_CR23","doi-asserted-by":"publisher","first-page":"2091","DOI":"10.1109\/JBHI.2018.2878878","volume":"23","author":"N Meng","year":"2018","unstructured":"Meng N, Lam EY, Tsia KK, So HK-H, et al: Large-scale multi-class image-based cell classification with deep learning. IEEE journal of biomedical and health informatics 23:2091-2098, 2018","journal-title":"IEEE journal of biomedical and health informatics"},{"key":"442_CR24","unstructured":"Pitk\u00e4aho T, Lehtim\u00e4ki TM, McDonald J, Naughton TJ, et al: Classifying HER2 breast cancer cell samples using deep learning. Proc. Proc Irish Mach Vis Image Process Conf: City"},{"key":"442_CR25","doi-asserted-by":"publisher","first-page":"45938","DOI":"10.1038\/srep45938","volume":"7","author":"ME Vandenberghe","year":"2017","unstructured":"Vandenberghe ME, Scott ML, Scorer PW, S\u00f6derberg M, Balcerzak D, Barker C, et al: Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer. Scientific reports 7:45938, 2017","journal-title":"Scientific reports"},{"key":"442_CR26","doi-asserted-by":"publisher","first-page":"2189","DOI":"10.1109\/TIP.2018.2795742","volume":"27","author":"M Saha","year":"2018","unstructured":"Saha M, Chakraborty C: Her2net: A deep framework for semantic segmentation and classification of cell membranes and nuclei in breast cancer evaluation. IEEE Transactions on Image Processing 27:2189-2200, 2018","journal-title":"IEEE Transactions on Image Processing"},{"key":"442_CR27","doi-asserted-by":"crossref","unstructured":"Khameneh FD, Razavi S, Kamasak M, et al: Automated segmentation of cell membranes to evaluate HER2 status in whole slide images using a modified deep learning network. Computers in biology and medicine, 2019","DOI":"10.1016\/j.compbiomed.2019.05.020"},{"key":"442_CR28","doi-asserted-by":"publisher","first-page":"2620","DOI":"10.1109\/TMI.2019.2907049","volume":"38","author":"T Qaiser","year":"2019","unstructured":"Qaiser T, Rajpoot NM: Learning where to see: A novel attention model for automated immunohistochemical scoring. IEEE transactions on medical imaging 38:2620-2631, 2019","journal-title":"IEEE transactions on medical imaging"},{"key":"442_CR29","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L, et al: Imagenet: A large-scale hierarchical image database. Proc.  IEEE conference on computer vision and pattern recognition: City,2009","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"442_CR30","unstructured":"Huh M, Agrawal P, Efros AA, et al: What makes ImageNet good for transfer learning? arXiv preprint https:\/\/arxiv.org\/1608.08614, 2016"},{"key":"442_CR31","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.ebiom.2017.12.026","volume":"27","author":"P Khosravi","year":"2018","unstructured":"Khosravi P, Kazemi E, Imielinski M, Elemento O, Hajirasouliha I, et al: Deep convolutional neural networks enable discrimination of heterogeneous digital pathology images. EBioMedicine 27:317-328, 2018","journal-title":"EBioMedicine"},{"key":"442_CR32","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/s10278-016-9929-2","volume":"30","author":"PM Cheng","year":"2017","unstructured":"Cheng PM, Malhi HS: Transfer learning with convolutional neural networks for classification of abdominal ultrasound images. Journal of digital imaging 30:234-243, 2017","journal-title":"Journal of digital imaging"},{"key":"442_CR33","unstructured":"He K, Zhang X, Ren S, Sun J, et al: Deep residual learning for image recognition. Proc. Proceedings of the IEEE conference on computer vision and pattern recognition: City"},{"key":"442_CR34","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C, et al: Mobilenetv2: Inverted residuals and linear bottlenecks. Proc. Proceedings of the IEEE conference on computer vision and pattern recognition: City"},{"key":"442_CR35","unstructured":"Zoph B, Vasudevan V, Shlens J, Le QV, et al: Learning transferable architectures for scalable image recognition. Proc. Proceedings of the IEEE conference on computer vision and pattern recognition: City"},{"key":"442_CR36","unstructured":"Simonyan K, Zisserman A: Very deep convolutional networks for large-scale image recognition. arXiv preprint https:\/\/arxiv.org\/1409.1556, 2014"},{"key":"442_CR37","unstructured":"Cire\u015fan DC, Giusti A, Gambardella LM, Schmidhuber J, et al: Mitosis detection in breast cancer histology images with deep neural networks. Proc. International conference on medical image computing and computer-assisted intervention: City"},{"key":"442_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patrec.2019.03.022","volume":"125","author":"S Khan","year":"2019","unstructured":"Khan S, Islam N, Jan Z, Din IU, Rodrigues JJC, et al: A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognition Letters 125:1-6, 2019","journal-title":"Pattern Recognition Letters"},{"key":"442_CR39","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","volume":"45","author":"M Sokolova","year":"2009","unstructured":"Sokolova M, Lapalme G: A systematic analysis of performance measures for classification tasks. Information processing & management 45:427-437, 2009","journal-title":"Information processing & management"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-021-00442-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-021-00442-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-021-00442-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,2]],"date-time":"2021-08-02T17:09:31Z","timestamp":1627924171000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-021-00442-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,19]]},"references-count":39,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["442"],"URL":"https:\/\/doi.org\/10.1007\/s10278-021-00442-5","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,19]]},"assertion":[{"value":"10 May 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 March 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}