{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T01:52:40Z","timestamp":1768269160475,"version":"3.49.0"},"reference-count":59,"publisher":"Elsevier BV","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["SSRN Journal"],"DOI":"10.2139\/ssrn.4019923","type":"journal-article","created":{"date-parts":[[2022,2,3]],"date-time":"2022-02-03T19:26:10Z","timestamp":1643916370000},"source":"Crossref","is-referenced-by-count":3,"title":["HEROHE Challenge: Assessing HER2 Status in Breast Cancer Without Immunohistochemistry or  &lt;i&gt;In Situ&lt;\/i&gt; Hybridization"],"prefix":"10.2139","author":[{"given":"Eduardo","family":"Conde-Sousa","sequence":"first","affiliation":[]},{"given":"Jo\u00e3o","family":"Vale","sequence":"additional","affiliation":[]},{"given":"Ming","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Kele","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Yin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Vincenzo","family":"Della Mea","sequence":"additional","affiliation":[]},{"given":"David","family":"La Barbera","sequence":"additional","affiliation":[]},{"given":"Ehsan","family":"Montahaei","sequence":"additional","affiliation":[]},{"given":"Mahdieh Soleymani","family":"Baghshah","sequence":"additional","affiliation":[]},{"given":"Andreas","family":"Turzynski","sequence":"additional","affiliation":[]},{"given":"Jacob","family":"Gildenblat","sequence":"additional","affiliation":[]},{"given":"Eldad","family":"Klaiman","sequence":"additional","affiliation":[]},{"given":"Yiyu","family":"Hong","sequence":"additional","affiliation":[]},{"given":"Guilherme","family":"Aresta","sequence":"additional","affiliation":[]},{"given":"Teresa","family":"Ara\u00fajo","sequence":"additional","affiliation":[]},{"given":"Paulo","family":"Aguiar","sequence":"additional","affiliation":[]},{"given":"Catarina","family":"Eloy","sequence":"additional","affiliation":[]},{"given":"Ant\u00f3nio","family":"Pol\u00f3nia","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"6","key":"ref1","doi-asserted-by":"crossref","first-page":"394","DOI":"10.3322\/caac.21492","article-title":"Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"F Bray","year":"2018","journal-title":"CA Cancer J Clin"},{"key":"ref2","volume":"2","author":"D Creyten","journal-title":"WHO classification of tumours : breast tumours"},{"key":"ref3","first-page":"545","volume":"144","author":"K H Allison","year":"2020","journal-title":"Estrogen and Progesterone Receptor Testing in Breast Cancer"},{"issue":"11","key":"ref4","first-page":"1364","article-title":"College of American Pathologists Clinical Practice Guideline Focused Update","volume":"142","author":"A C Wolff","year":"2018","journal-title":"Arch Pathol Lab Med"},{"issue":"11","key":"ref5","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1056\/NEJM200103153441101","article-title":"Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2","volume":"344","author":"D J Slamon","year":"2001","journal-title":"N Engl J Med"},{"issue":"3","key":"ref6","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1200\/JCO.2002.20.3.719","article-title":"Efficacy and safety of trastuzumab as a single agent in first-line treatment of HER2-overexpressing metastatic breast cancer","volume":"20","author":"C L Vogel","year":"2002","journal-title":"J Clin Oncol"},{"issue":"16","key":"ref7","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1056\/NEJMoa052306","article-title":"Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer","volume":"353","author":"M J Piccart-Gebhart","year":"2005","journal-title":"N Engl J Med"},{"key":"ref8","article-title":"Cost-effectiveness of a Dual (Immunohistochemistry and Fluorescence In Situ Hybridization) HER2\/neu Testing Strategy on Invasive Breast Cancers","author":"N Hariri","year":"2020","journal-title":"Appl Immunohistochem Mol Morphol"},{"issue":"3","key":"ref9","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/s00428-018-02519-z","article-title":"The performance of digital microscopy for primary diagnosis in human pathology: a systematic review","volume":"474","author":"A L D Araujo","year":"2019","journal-title":"Virchows Arch"},{"issue":"5","key":"ref10","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s10278-017-9946-9","article-title":"Enterprise Implementation of Digital Pathology: Feasibility, Challenges, and Opportunities","volume":"30","author":"D J Hartman","year":"2017","journal-title":"J Digit Imaging"},{"key":"ref11","article-title":"Multi-input convolutional neural network for breast cancer detection using thermal images and clinical data","author":"R Sanchez-Cauce","year":"2021","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"10","key":"ref12","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1016\/j.compbiomed.2013.08.003","article-title":"Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images","volume":"43","author":"M Kowal","year":"2013","journal-title":"Comput Biol Med"},{"issue":"3","key":"ref13","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1109\/JSYST.2013.2279415","article-title":"Remote Computer-Aided Breast Cancer Detection and Diagnosis System Based on Cytological Images","volume":"8","author":"Y M George","year":"2014","journal-title":"IEEE Systems Journal"},{"key":"ref14","article-title":"Breast cancer diagnosis from biopsy images by serial fusion of Random Subspace ensembles","author":"B Zhang","year":"2011","journal-title":"2011 4th International Conference on Biomedical Engineering and Informatics (BMEI)"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.compbiomed.2018.03.003","article-title":"Automatic classification of tissue malignancy for breast carcinoma diagnosis","volume":"96","author":"I Fondon","year":"2018","journal-title":"Comput Biol Med"},{"issue":"6","key":"ref16","article-title":"Classification of breast cancer histology images using Convolutional Neural Networks","volume":"12","author":"T Ara\ufffdjo","year":"2017","journal-title":"PLOS ONE"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.cmpb.2019.03.006","article-title":"Automated density-based counting of FISH amplification signals for HER2 status assessment","volume":"173","author":"H Hofener","year":"2019","journal-title":"Comput Methods Programs Biomed"},{"key":"ref18","volume":"7","author":"M E Vandenberghe","year":"2017","journal-title":"Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.compbiomed.2019.05.020","article-title":"Automated segmentation of cell membranes to evaluate HER2 status in whole slide images using a modified deep learning network","volume":"110","author":"F D Khameneh","year":"2019","journal-title":"Computers in Biology and Medicine"},{"key":"ref20","doi-asserted-by":"crossref","DOI":"10.1038\/s41523-018-0079-1","article-title":"Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype","volume":"4","author":"H D Couture","year":"2018","journal-title":"NPJ Breast Cancer"},{"issue":"7","key":"ref21","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2019.7700","article-title":"Artificial Intelligence Algorithms to Assess Hormonal Status From Tissue Microarrays in Patients With Breast Cancer","volume":"2","author":"G Shamai","year":"2019","journal-title":"JAMA Netw Open"},{"key":"ref22","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2016.90","article-title":"Deep Residual Learning for Image Recognition","author":"K He","year":"2016","journal-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"},{"issue":"1","key":"ref23","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-020-19334-3","article-title":"Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains","volume":"11","author":"N Naik","year":"2020","journal-title":"Nat Commun"},{"issue":"1","key":"ref24","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1038\/s43018-020-0087-6","article-title":"Pan-cancer image-based detection of clinically actionable genetic alterations","author":"J N Kather","year":"2020","journal-title":"Nature Cancer"},{"key":"ref25","article-title":"Shufflenet: An extremely efficient convolutional neural network for mobile devices","author":"X Zhang","year":"2018","journal-title":"Proceedings of the IEEE conference on computer vision and pattern recognition"},{"issue":"1","key":"ref26","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41379-021-00911-w","article-title":"Deep learning trained on hematoxylin and eosin tumor region of Interest predicts HER2 status and trastuzumab treatment response in HER2+breast cancer","volume":"35","author":"S Farahmand","year":"2022","journal-title":"Modern Pathology"},{"issue":"2","key":"ref27","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1111\/his.13333","article-title":"HER2 challenge contest: a detailed assessment of automated HER2 scoring algorithms in whole slide images of breast cancer tissues","volume":"72","author":"T Qaiser","year":"2018","journal-title":"Histopathology"},{"key":"ref28","author":"G Litjens","year":"1399","journal-title":"H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset"},{"issue":"22","key":"ref29","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1001\/jama.2017.14585","article-title":"Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer","volume":"318","author":"B Ehteshami Bejnordi","year":"2017","journal-title":"JAMA"},{"key":"ref30","first-page":"122","volume":"56","author":"G Aresta","year":"2019","journal-title":"BACH: Grand challenge on breast cancer histology images. Med Image Anal"},{"key":"ref31","doi-asserted-by":"crossref","DOI":"10.4103\/jpi.jpi_64_19","article-title":"Value of Public Challenges for the Development of Pathology Deep Learning Algorithms","volume":"11","author":"D J Hartman","year":"2020","journal-title":"J Pathol Inform"},{"issue":"3","key":"ref32","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1002\/ijc.21004","article-title":"High-throughput protein expression analysis using tissue microarray technology of a large well-characterised series identifies biologically distinct classes of breast cancer confirming recent cDNA expression analyses","volume":"116","author":"D M El-Rehim","year":"2005","journal-title":"Int J Cancer"},{"issue":"21","key":"ref33","doi-asserted-by":"crossref","first-page":"2492","DOI":"10.1001\/jama.295.21.2492","article-title":"Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study","volume":"295","author":"L A Carey","year":"2006","journal-title":"JAMA"},{"issue":"4","key":"ref34","doi-asserted-by":"crossref","DOI":"10.1186\/bcr2128","article-title":"Comparison of molecular phenotypes of ductal carcinoma in situ and invasive breast cancer","volume":"10","author":"R M Tamimi","year":"2008","journal-title":"Breast Cancer Res"},{"issue":"9","key":"ref35","doi-asserted-by":"crossref","first-page":"1638","DOI":"10.1038\/s41379-020-0526-z","article-title":"A machine learning algorithm for simulating immunohistochemistry: development of SOX10 virtual IHC and evaluation on primarily melanocytic neoplasms","volume":"33","author":"C R Jackson","year":"2020","journal-title":"Mod Pathol"},{"issue":"2","key":"ref36","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1109\/JBHI.2020.2975151","article-title":"Seamless Virtual Whole Slide Image Synthesis and Validation Using Perceptual Embedding Consistency","volume":"25","author":"A Lahiani","year":"2021","journal-title":"IEEE J Biomed Health Inform"},{"key":"ref37","author":"Z Xu","year":"2019","journal-title":"GAN-based virtual re-staining: a promising solution for whole slide image analysis"},{"issue":"8","key":"ref38","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"T Fawcett","year":"2006","journal-title":"Pattern Recognition Letters"},{"key":"ref39","article-title":"Computer-aided Detection of Squamous Carcinoma of the Cervix in Whole Slide Images","author":"Y Tian","year":"1905","journal-title":"ArXiv"},{"issue":"6","key":"ref40","article-title":"Detection of HER2 from Haematoxylin-Eosin Slides Through a Cascade of Deep Learning Classifiers via Multi-Instance Learning","author":"La Barbera","year":"2020","journal-title":"Journal of Imaging"},{"key":"ref41","article-title":"Densely connected convolutional networks","author":"G Huang","year":"2017","journal-title":"Proceedings of the IEEE conference on computer vision and pattern recognition"},{"key":"ref42","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2009.5206848","article-title":"ImageNet: A large-scale hierarchical image database","author":"J Deng","year":"2009","journal-title":"2009 IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"ref43","first-page":"532","volume":"55","author":"C A Glasbey","year":"1993","journal-title":"An Analysis of Histogram-Based Thresholding Algorithms. CVGIP: Graphical Models and Image Processing"},{"key":"ref44","first-page":"6105","article-title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","author":"M Tan","year":"2019","journal-title":"Proceedings of the 36th International Conference on Machine Learning, C. Kamalika and S. Ruslan"},{"key":"ref45","author":"O Ronneberger","year":"2015","journal-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation"},{"key":"ref46","volume":"7","author":"P Bankhead","year":"2017","journal-title":"QuPath: Open source software for digital pathology image analysis. Sci Rep"},{"issue":"8","key":"ref47","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","article-title":"Clinical-grade computational pathology using weakly supervised deep learning on whole slide images","volume":"25","author":"G Campanella","year":"2019","journal-title":"Nat Med"},{"key":"ref48","author":"A Krizhevsky","year":"2012","journal-title":"Learning Multiple Layers of Features from Tiny Images"},{"key":"ref49","article-title":"Aggregated residual transformations for deep neural networks","author":"S Xie","year":"2017","journal-title":"Proceedings of the IEEE conference on computer vision and pattern recognition"},{"key":"ref50","article-title":"Xception: Deep learning with depthwise separable convolutions","author":"F Chollet","year":"2017","journal-title":"Proceedings of the IEEE conference on computer vision and pattern recognition"},{"issue":"2","key":"ref51","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1309\/AJCPIXUYDVZ0RZ3G","article-title":"Prognostic Significance of Tumor-Infiltrating Lymphocytes and the Tertiary Lymphoid Structures in HER2-Positive Breast Cancer Treated With Adjuvant Trastuzumab","volume":"144","author":"H J Lee","year":"2015","journal-title":"Am J Clin Pathol"},{"issue":"4","key":"ref52","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1001\/jamaoncol.2015.0830","article-title":"Tumor-Infiltrating Lymphocytes and Associations With Pathological Complete Response and Event-Free Survival in HER2-Positive Early-Stage Breast Cancer Treated With Lapatinib and Trastuzumab: A Secondary Analysis of the NeoALTTO Trial","volume":"1","author":"R Salgado","year":"2015","journal-title":"JAMA Oncol"},{"key":"ref53","first-page":"1134","volume":"34","author":"L N Harris","year":"2016","journal-title":"Use of Biomarkers to Guide Decisions on Adjuvant Systemic Therapy for Women With Early-Stage Invasive Breast Cancer"},{"key":"ref54","first-page":"2838","article-title":"Oncology Clinical Practice Guideline Focused Update","volume":"35","author":"I Krop","year":"2017","journal-title":"Use of Biomarkers to Guide Decisions on Adjuvant Systemic Therapy for Women With Early-Stage Invasive Breast Cancer"},{"issue":"1","key":"ref55","doi-asserted-by":"crossref","DOI":"10.1186\/s12885-018-4448-9","article-title":"Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer","volume":"18","author":"J Whitney","year":"2018","journal-title":"BMC Cancer"},{"key":"ref56","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.csbj.2021.12.028","article-title":"Prediction of HER2-positive breast cancer recurrence and metastasis risk from histopathological images and clinical information via multimodal deep learning","volume":"20","author":"J Yang","year":"2022","journal-title":"Computational and Structural Biotechnology Journal"},{"issue":"4","key":"ref57","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1007\/s00428-016-1903-3","article-title":"Application of the 2013 ASCO\/CAP guideline and the SISH technique for HER2 testing of breast cancer selects more patients for anti-HER2 treatment","volume":"468","author":"A Polonia","year":"2016","journal-title":"Virchows Arch"},{"issue":"5","key":"ref58","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1007\/s00428-017-2189-9","article-title":"Characterization of HER2 gene amplification heterogeneity in invasive and in situ breast cancer using bright-field in situ hybridization","volume":"471","author":"A Polonia","year":"2017","journal-title":"Virchows Arch"},{"key":"ref59","article-title":"Yiyu Hong l , Guilherme Aresta m,n , Teresa Ara\ufffdjo m,n , Paulo Aguiar a,b , Catarina Eloy a,c,o , Ant\ufffdnio Pol\ufffdnia a,c a. I3S -Instituto de Investiga\ufffd\ufffdo e Inova\ufffd\ufffdo em Sa\ufffdde","volume":"5","author":"Jo\ufffdo Conde-Sousa A,B","journal-title":"HEROHE Challenge: assessing HER2 status in breast cancer without immunohistochemistry or in situ hybridization Authors Eduardo"}],"container-title":["SSRN Electronic Journal"],"original-title":[],"language":"en","deposited":{"date-parts":[[2023,3,25]],"date-time":"2023-03-25T02:55:53Z","timestamp":1679712953000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=4019923"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":59,"URL":"https:\/\/doi.org\/10.2139\/ssrn.4019923","relation":{},"ISSN":["1556-5068"],"issn-type":[{"value":"1556-5068","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022]]},"published":{"date-parts":[[2022]]}}}