{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T16:12:44Z","timestamp":1785946364839,"version":"3.56.0"},"reference-count":34,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T00:00:00Z","timestamp":1702339200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012190","name":"Ministry of Science and Higher Education of the Russian Federation","doi-asserted-by":"publisher","award":["075-15-2022-294"],"award-info":[{"award-number":["075-15-2022-294"]}],"id":[{"id":"10.13039\/501100012190","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012190","name":"Ministry of Science and Higher Education of the Russian Federation","doi-asserted-by":"publisher","award":["121032500100-3"],"award-info":[{"award-number":["121032500100-3"]}],"id":[{"id":"10.13039\/501100012190","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012190","name":"Ministry of Science and Higher Education of the Russian Federation","doi-asserted-by":"publisher","award":["22-75-00048"],"award-info":[{"award-number":["22-75-00048"]}],"id":[{"id":"10.13039\/501100012190","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017638","name":"Ministry of Health of the Russian Federation","doi-asserted-by":"publisher","award":["075-15-2022-294"],"award-info":[{"award-number":["075-15-2022-294"]}],"id":[{"id":"10.13039\/501100017638","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017638","name":"Ministry of Health of the Russian Federation","doi-asserted-by":"publisher","award":["121032500100-3"],"award-info":[{"award-number":["121032500100-3"]}],"id":[{"id":"10.13039\/501100017638","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017638","name":"Ministry of Health of the Russian Federation","doi-asserted-by":"publisher","award":["22-75-00048"],"award-info":[{"award-number":["22-75-00048"]}],"id":[{"id":"10.13039\/501100017638","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006769","name":"Russian Science Foundation","doi-asserted-by":"publisher","award":["075-15-2022-294"],"award-info":[{"award-number":["075-15-2022-294"]}],"id":[{"id":"10.13039\/501100006769","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006769","name":"Russian Science Foundation","doi-asserted-by":"publisher","award":["121032500100-3"],"award-info":[{"award-number":["121032500100-3"]}],"id":[{"id":"10.13039\/501100006769","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006769","name":"Russian Science Foundation","doi-asserted-by":"publisher","award":["22-75-00048"],"award-info":[{"award-number":["22-75-00048"]}],"id":[{"id":"10.13039\/501100006769","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>H-score is a semi-quantitative method used to assess the presence and distribution of proteins in tissue samples by combining the intensity of staining and the percentage of stained nuclei. It is widely used but time-consuming and can be limited in terms of accuracy and precision. Computer-aided methods may help overcome these limitations and improve the efficiency of pathologists\u2019 workflows. In this work, we developed a model EndoNet for automatic H-score calculation on histological slides. Our proposed method uses neural networks and consists of two main parts. The first is a detection model which predicts the keypoints of centers of nuclei. The second is an H-score module that calculates the value of the H-score using mean pixel values of predicted keypoints. Our model was trained and validated on 1780 annotated tiles with a shape of 100 \u00d7 100 \u00b5m and we achieved 0.77 mAP on a test dataset. We obtained our best results in H-score calculation; these results proved superior to QuPath predictions. Moreover, the model can be adjusted to a specific specialist or whole laboratory to reproduce the manner of calculating the H-score. Thus, EndoNet is effective and robust in the analysis of histology slides, which can improve and significantly accelerate the work of pathologists.<\/jats:p>","DOI":"10.3390\/informatics10040090","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T05:23:22Z","timestamp":1702358602000},"page":"90","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["EndoNet: A Model for the Automatic Calculation of H-Score on Histological Slides"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8370-6911","authenticated-orcid":false,"given":"Egor","family":"Ushakov","sequence":"first","affiliation":[{"name":"Information Systems Department, Ivannikov Institute for System Programming of the Russian Academy of Sciences (ISP RAS), 109004 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4851-7677","authenticated-orcid":false,"given":"Anton","family":"Naumov","sequence":"additional","affiliation":[{"name":"Information Systems Department, Ivannikov Institute for System Programming of the Russian Academy of Sciences (ISP RAS), 109004 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5551-7590","authenticated-orcid":false,"given":"Vladislav","family":"Fomberg","sequence":"additional","affiliation":[{"name":"Information Systems Department, Ivannikov Institute for System Programming of the Russian Academy of Sciences (ISP RAS), 109004 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8650-8240","authenticated-orcid":false,"given":"Polina","family":"Vishnyakova","sequence":"additional","affiliation":[{"name":"FSBI \u201cNational Medical Research Centre for Obstetrics, Gynecology and Perinatology Named after Academician V.I.Kulakov\u201d, Ministry of Health of the Russian Federation, 4, Oparina Street, 117997 Moscow, Russia"},{"name":"Research Institute of Molecular and Cellular Medicine, Peoples\u2019 Friendship University of Russia (RUDN University), Miklukho-Maklaya Street 6, 117198 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8739-5209","authenticated-orcid":false,"given":"Aleksandra","family":"Asaturova","sequence":"additional","affiliation":[{"name":"FSBI \u201cNational Medical Research Centre for Obstetrics, Gynecology and Perinatology Named after Academician V.I.Kulakov\u201d, Ministry of Health of the Russian Federation, 4, Oparina Street, 117997 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5223-9767","authenticated-orcid":false,"given":"Alina","family":"Badlaeva","sequence":"additional","affiliation":[{"name":"FSBI \u201cNational Medical Research Centre for Obstetrics, Gynecology and Perinatology Named after Academician V.I.Kulakov\u201d, Ministry of Health of the Russian Federation, 4, Oparina Street, 117997 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4601-1330","authenticated-orcid":false,"given":"Anna","family":"Tregubova","sequence":"additional","affiliation":[{"name":"FSBI \u201cNational Medical Research Centre for Obstetrics, Gynecology and Perinatology Named after Academician V.I.Kulakov\u201d, Ministry of Health of the Russian Federation, 4, Oparina Street, 117997 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6771-2163","authenticated-orcid":false,"given":"Evgeny","family":"Karpulevich","sequence":"additional","affiliation":[{"name":"Information Systems Department, Ivannikov Institute for System Programming of the Russian Academy of Sciences (ISP RAS), 109004 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7712-1260","authenticated-orcid":false,"given":"Gennady","family":"Sukhikh","sequence":"additional","affiliation":[{"name":"FSBI \u201cNational Medical Research Centre for Obstetrics, Gynecology and Perinatology Named after Academician V.I.Kulakov\u201d, Ministry of Health of the Russian Federation, 4, Oparina Street, 117997 Moscow, 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scoring","volume":"7","author":"Rizzardi","year":"2012","journal-title":"Diagn. Pathol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"101813","DOI":"10.1016\/j.media.2020.101813","article-title":"Deep neural network models for computational histopathology: A survey","volume":"67","author":"Srinidhi","year":"2021","journal-title":"Med Image Anal."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1504","DOI":"10.1038\/s41598-020-58467-9","article-title":"Deep Learning Models for Histopathological Classification of Gastric and Colonic Epithelial Tumours","volume":"10","author":"Iizuka","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_4","first-page":"5419","article-title":"Immunohistochemical analyses of estrogen receptor in endometrial adenocarcinoma using a monoclonal antibody","volume":"46","author":"McCarty","year":"1986","journal-title":"Cancer Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1016\/0277-5379(87)90117-9","article-title":"Multiple microsample analysis of intratumor estrogen receptor distribution in breast cancers by a combined biochemical\/immunohistochemical method","volume":"23","author":"Thornton","year":"1987","journal-title":"Eur. J. Cancer Clin. Oncol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6","DOI":"10.4103\/jiaps.JIAPS_166_18","article-title":"Androgen receptor expression in hypospadias","volume":"25","author":"Babu","year":"2020","journal-title":"J. Indian Assoc. Pediatr. Surg."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1155\/2011\/583182","article-title":"Strategies for H-score Normalization of Preanalytical Technical Variables with Potential Utility to Immunohistochemical-Based Biomarker Quantitation in Therapeutic Reponse Diagnostics","volume":"34","author":"Pierceall","year":"2011","journal-title":"Anal. Cell. Pathol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"96","DOI":"10.4103\/jofs.jofs_141_21","article-title":"Histoscore and Discontinuity Score - A Novel Scoring System to Evaluate Immunohistochemical Expression of COX-2 and Type IV Collagen in Oral Potentially Malignant Disorders and Oral Squamous Cell Carcinoma","volume":"13","author":"Sharada","year":"2021","journal-title":"J. Orofac. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ram, S., Vizcarra, P., Whalen, P., Deng, S., Painter, C.L., Jackson-Fisher, A., Pirie-Shepherd, S., Xia, X., and Powell, E.L. (2021). Pixelwise H-score: A novel digital image analysis-based metric to quantify membrane biomarker expression from immunohistochemistry images. PLoS ONE, 16.","DOI":"10.1101\/2021.01.06.425539"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"40","DOI":"10.4103\/jpi.jpi_69_18","article-title":"Twenty Years of Digital Pathology: An Overview of the Road Travelled, What is on the Horizon, and the Emergence of Vendor-Neutral Archives","volume":"9","author":"Pantanowitz","year":"2018","journal-title":"J. Pathol. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/RBME.2009.2034865","article-title":"Histopathological Image Analysis: A Review","volume":"2","author":"Gurcan","year":"2009","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.media.2016.06.037","article-title":"Image analysis and machine learning in digital pathology: Challenges and opportunities","volume":"33","author":"Madabhushi","year":"2016","journal-title":"Med Image Anal."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1400","DOI":"10.1109\/TBME.2014.2303852","article-title":"Breast Cancer Histopathology Image Analysis: A Review","volume":"61","author":"Veta","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4821","DOI":"10.1007\/s10462-020-09808-7","article-title":"A review for cervical histopathology image analysis using machine vision approaches","volume":"53","author":"Li","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_15","unstructured":"Pereira, F., Burges, C., Bottou, L., and Weinberger, K. (2012). Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 11\u201318). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_17","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"102142","DOI":"10.1016\/j.media.2021.102142","article-title":"SRPN: Similarity-based region proposal networks for nuclei and cells detection in histology images","volume":"72","author":"Sun","year":"2021","journal-title":"Med Image Anal."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_20","unstructured":"Avranas, A., and Kountouris, M. (2022). Coded ResNeXt: A network for designing disentangled information paths. arXiv."},{"key":"ref_21","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Naumov, A., Ushakov, E., Ivanov, A., Midiber, K., Khovanskaya, T., Konyukova, A., Vishnyakova, P., Nora, S., Mikhaleva, L., and Fatkhudinov, T. (2022). EndoNuke: Nuclei Detection Dataset for Estrogen and Progesterone Stained IHC Endometrium Scans. Data, 7.","DOI":"10.3390\/data7060075"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ronchi, M.R., and Perona, P. (2017, January 22\u201329). Benchmarking and Error Diagnosis in Multi-instance Pose Estimation. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.48"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1002\/nav.3800020109","article-title":"The Hungarian method for the assignment problem","volume":"2","author":"Kuhn","year":"1955","journal-title":"Nav. Res. Logist. Q."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A Coefficient of Agreement for Nominal Scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"104117","DOI":"10.1016\/j.imavis.2021.104117","article-title":"Weighted boxes fusion: Ensembling boxes from different object detection models","volume":"107","author":"Solovyev","year":"2021","journal-title":"Image Vis. Comput."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017). Focal Loss for Dense Object Detection. arXiv.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_28","unstructured":"Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. arXiv."},{"key":"ref_29","unstructured":"Ushakov, E., Naumov, A., and Fomberg, V. (2023, November 26). EndoNet: Code and Weights. Available online: https:\/\/github.com\/ispras\/endonet."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1109\/JBHI.2019.2944977","article-title":"Computer-Aided Diagnosis in Histopathological Images of the Endometrium Using a Convolutional Neural Network and Attention Mechanisms","volume":"24","author":"Sun","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1049\/iet-ipr.2018.6513","article-title":"Generalising multistain immunohistochemistry tissue segmentation using end-to-end colour deconvolution deep neural networks","volume":"13","author":"Lahiani","year":"2019","journal-title":"IET Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.compmedimag.2017.06.001","article-title":"Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology","volume":"61","author":"Sharma","year":"2017","journal-title":"Comput. Med Imaging Graph."},{"key":"ref_33","unstructured":"Chen, T., and Chefd\u2019hotel, C. (2014). Machine Learning in Medical Imaging, Springer International Publishing."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1369\/jhc.2009.952812","article-title":"Image Analysis Algorithms for Immunohistochemical Assessment of Cell Death Events and Fibrosis in Tissue Sections","volume":"57","author":"Krajewska","year":"2009","journal-title":"J. Histochem. Cytochem."}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/10\/4\/90\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:37:16Z","timestamp":1760132236000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/10\/4\/90"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,12]]},"references-count":34,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["informatics10040090"],"URL":"https:\/\/doi.org\/10.3390\/informatics10040090","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,12]]}}}