{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T23:24:25Z","timestamp":1788305065774,"version":"build-2803163510"},"reference-count":61,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T00:00:00Z","timestamp":1685318400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1016\/j.cmpb.2023.107629","type":"journal-article","created":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T04:24:28Z","timestamp":1685507068000},"page":"107629","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":26,"special_numbering":"C","title":["Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks"],"prefix":"10.1016","volume":"240","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1072-6142","authenticated-orcid":false,"given":"Kevin","family":"Barrera","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1514-7713","authenticated-orcid":false,"given":"Jos\u00e9","family":"Rodellar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8661-1096","authenticated-orcid":false,"given":"Santiago","family":"Alf\u00e9rez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1889-8889","authenticated-orcid":false,"given":"Anna","family":"Merino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.cmpb.2023.107629_bib0001","article-title":"Manual de citolog\u00eda de sangre perif\u00e9rica y l\u00edquidos biol\u00f3gicos","author":"Merino","year":"2019","journal-title":"Editor. Panamericana"},{"issue":"4","key":"10.1016\/j.cmpb.2023.107629_bib0002","doi-asserted-by":"crossref","first-page":"174","DOI":"10.4103\/joc.joc_43_22","article-title":"Comparison of modified ultrafast giemsa stain with the standard may grunwald giemsa stain in FNAC of various organs","volume":"39","author":"Deepthi","year":"2022","journal-title":"J. Cytol."},{"key":"10.1016\/j.cmpb.2023.107629_bib0003","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1007\/BF00158587","article-title":"Standardization of reagents and methods used in cytological and histological practice with emphasis on dyes, stains and chromogenic reagents","volume":"26","author":"Lyon","year":"2004","journal-title":"Histochem. J."},{"key":"10.1016\/j.cmpb.2023.107629_sbref0004","doi-asserted-by":"crossref","first-page":"100145","DOI":"10.1016\/j.jpi.2022.100145","article-title":"Stain normalization in digital pathology: clinical multi-center evaluation of image quality","volume":"13","author":"Michielli","year":"2022","journal-title":"J. Pathol. Inform."},{"key":"10.1016\/j.cmpb.2023.107629_bib0005","first-page":"1503","article-title":"Impact of stain normalization on pathologist assessment of prostate cancer: a comparative study","volume":"15","author":"Salvi","year":"2023","journal-title":"Cancers 2023, Vol. 15, Page 1503"},{"key":"10.1016\/j.cmpb.2023.107629_bib0006","doi-asserted-by":"crossref","first-page":"101544","DOI":"10.1016\/j.media.2019.101544","article-title":"Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology","volume":"58","author":"Tellez","year":"2019","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.cmpb.2023.107629_bib0007","doi-asserted-by":"crossref","first-page":"105020","DOI":"10.1016\/j.cmpb.2019.105020","article-title":"Recognition of peripheral blood cell images using convolutional neural networks","volume":"180","author":"Acevedo","year":"2019","journal-title":"Comput. Methods Programs. Biomed."},{"key":"10.1016\/j.cmpb.2023.107629_bib0008","doi-asserted-by":"crossref","first-page":"105999","DOI":"10.1016\/j.cmpb.2021.105999","article-title":"A deep learning model (ALNet) for the diagnosis of acute leukaemia lineage using peripheral blood cell images","volume":"202","author":"Bold\u00fa","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.cmpb.2023.107629_bib0009","doi-asserted-by":"crossref","first-page":"105020","DOI":"10.1016\/j.compbiomed.2021.104479","article-title":"A new convolutional neural network predictive model for the automatic recognition of hypogranulated neutrophils in myelodysplastic syndromes","volume":"134","author":"Acevedo","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.cmpb.2023.107629_bib0010","doi-asserted-by":"crossref","first-page":"104680","DOI":"10.1016\/j.compbiomed.2021.104680","article-title":"Automatic identification of malaria and other red blood cell inclusions using convolutional neural networks","volume":"136","author":"Molina","year":"2021","journal-title":"Comput. Biol. Med."},{"issue":"4","key":"10.1016\/j.cmpb.2023.107629_bib0011","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1111\/ijlh.12648","article-title":"The use of cellavision\u00e2 c=\"bad accent\" l=\"1117\" n=\"\\v\"\/>\u0107 DM 96 in the verification of the presence of blasts in samples flagged by the sysmex XE-5000","volume":"39","author":"Eilertsen","year":"2017","journal-title":"Int. J. Lab. Hematol."},{"key":"10.1016\/j.cmpb.2023.107629_bib0012","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1111\/ijlh.12832","article-title":"Optimizing morphology through blood cell image analysis","volume":"40","author":"Merino","year":"2018","journal-title":"Int. J. Lab. Hematol."},{"key":"10.1016\/j.cmpb.2023.107629_bib0013","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.cmpb.2023.107629_bib0014","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"Radford","year":"2015","journal-title":"4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings"},{"key":"10.1016\/j.cmpb.2023.107629_bib0015","unstructured":"M. Mirza, S. Osindero, Conditional generative adversarial nets (2014). doi:10.48550\/arxiv.1411.1784."},{"key":"10.1016\/j.cmpb.2023.107629_bib0016","unstructured":"M. Arjovsky, S. Chintala, L. Bottou, Wasserstein GAN (2017). https:\/\/arxiv.org\/abs\/1701.07875v3."},{"key":"10.1016\/j.cmpb.2023.107629_bib0017","series-title":"Proceedings of the IEEE International Conference on Computer Vision (ICCV)","article-title":"Unpaired Image-To-Image translation using cycle-consistent adversarial networks","author":"Zhu","year":"2017"},{"key":"10.1016\/j.cmpb.2023.107629_bib0018","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"4681","article-title":"Photo-realistic single image super-resolution using a generative adversarial network","author":"Ledig","year":"2017"},{"key":"10.1016\/j.cmpb.2023.107629_bib0019","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"8789","article-title":"Stargan: unified generative adversarial networks for multi-domain image-to-image translation","author":"Choi","year":"2018"},{"key":"10.1016\/j.cmpb.2023.107629_bib0020","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"1125","article-title":"Image-to-image translation with conditional adversarial networks","author":"Isola","year":"2017"},{"issue":"July","key":"10.1016\/j.cmpb.2023.107629_bib0021","doi-asserted-by":"crossref","first-page":"101938","DOI":"10.1016\/j.artmed.2020.101938","article-title":"GANs For medical image analysis","volume":"109","author":"Kazeminia","year":"2020","journal-title":"Artif. Intell. Med."},{"issue":"37","key":"10.1016\/j.cmpb.2023.107629_bib0022","doi-asserted-by":"crossref","first-page":"27583","DOI":"10.1007\/s11042-020-09299-2","article-title":"Color vision deficiency datasets & recoloring evaluation using GANs","volume":"79","author":"Li","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.cmpb.2023.107629_bib0023","doi-asserted-by":"crossref","first-page":"106822","DOI":"10.1016\/j.asoc.2020.106822","article-title":"Cycle-consistent GAN-based stain translation of renal pathology images with glomerulus detection application","volume":"98","author":"Lo","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.cmpb.2023.107629_bib0024","first-page":"arXiv","article-title":"StainGAN: stain style transfer for digital histological images","author":"Tarek Shaban","year":"2018","journal-title":"arXiv e-prints"},{"key":"10.1016\/j.cmpb.2023.107629_bib0025","first-page":"2002","article-title":"Stainnet: a fast and robust stain normalization network","volume":"8","author":"Kang","year":"2021","journal-title":"Front. Med. (Lausanne)"},{"key":"10.1016\/j.cmpb.2023.107629_bib0026","series-title":"International Conference on Machine Learning and Intelligent Communications","first-page":"137","article-title":"The generation of virtual immunohistochemical staining images based on an improved cycle-gan","author":"Liu","year":"2021"},{"key":"10.1016\/j.cmpb.2023.107629_bib0027","article-title":"Neural stain normalization and unsupervised classification of cell nuclei in histopathological breast cancer images","author":"Yuan","year":"2018","journal-title":"arXiv preprint arXiv:1811.03815"},{"key":"10.1016\/j.cmpb.2023.107629_bib0028","doi-asserted-by":"crossref","unstructured":"H.R. Roth, K. Chang, P. Singh, N. Neumark, W. Li, V. Gupta, S. Gupta, L. Qu, A. Ihsani, B.C. Bizzo, Y. Wen, V. Buch, M. Shah, F. Kitamura, M. Mendon\u00e7a, V. Lavor, A. Harouni, C. Compas, J. Tetreault, P. Dogra, Y. Cheng, S. Erdal, R. White, B. Hashemian, T. Schultz, M. Zhang, A. McCarthy, B.M. Yun, E. Sharaf, K.V. Hoebel, J.B. Patel, B. Chen, S. Ko, E. Leibovitz, E.D. Pisano, L. Coombs, D. Xu, K.J. Dreyer, I. Dayan, R.C. Naidu, M. Flores, D. Rubin, J. Kalpathy-Cramer, Federated learning for breast density classification: A real-world implementation (2020). doi:10.1007\/978-3-030-60548-318.","DOI":"10.1007\/978-3-030-60548-3_18"},{"key":"10.1016\/j.cmpb.2023.107629_bib0029","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1016\/j.procs.2021.02.101","article-title":"Cells image generation method based on VAE-SGAN","volume":"183","author":"Liu","year":"2021","journal-title":"Procedia Comput. Sci."},{"issue":"2","key":"10.1016\/j.cmpb.2023.107629_bib0030","first-page":"276","article-title":"Synthesis of microscopic cell images obtained from bone marrow aspirate smears through generative adversarial networks","volume":"11","author":"Hazra","year":"2022","journal-title":"Biology (Basel)"},{"key":"10.1016\/j.cmpb.2023.107629_bib0031","doi-asserted-by":"crossref","first-page":"107314","DOI":"10.1016\/j.cmpb.2022.107314","article-title":"Automatic generation of artificial images of leukocytes and leukemic cells using generative adversarial networks (syntheticcellGAN)","volume":"229","author":"Barrera","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.cmpb.2023.107629_bib0032","series-title":"2019 International Conference on Automation, Computational and Technology Management (ICACTM)","first-page":"19","article-title":"A stain normalization method for the white blood cell image","author":"Liu","year":"2019"},{"key":"10.1016\/j.cmpb.2023.107629_bib0033","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1109\/TMI.2020.3046334","article-title":"Multi-task multi-domain learning for digital staining and classification of leukocytes","volume":"40","author":"Tomczak","year":"2021","journal-title":"IEEE Trans. Med. Imag."},{"issue":"6","key":"10.1016\/j.cmpb.2023.107629_bib0034","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1111\/ijlh.12720","article-title":"Comparing the performance of three panels rules of blood smear review criteria on an italian multicenter evaluation","volume":"39","author":"Pipitone","year":"2017","journal-title":"Int. J. Lab. Hematol."},{"key":"10.1016\/j.cmpb.2023.107629_bib0035","first-page":"23","article-title":"The sysmex SP1000i for automated bone marrow slide smear staining","volume":"40","author":"Gemen","year":"2009","journal-title":"LABMEDICINE"},{"key":"10.1016\/j.cmpb.2023.107629_bib0036","first-page":"35","article-title":"Examen de pel\u00edculas de sangre perif\u00e9rica utilizando microscop\u00eda automatizada; evaluaci\u00f3n de diffmaster octavia y cellavision DM96","volume":"60","author":"Ceelie","year":"2007","journal-title":"Revista de patolog\u00eda cl\u00ednica"},{"key":"10.1016\/j.cmpb.2023.107629_bib0037","series-title":"Diagnosis and Management of Myelodysplastic Syndromes","first-page":"19","article-title":"Morphological, flow cytometry, and cytogenetic diagnosis of MDS","author":"Goel","year":"2020"},{"key":"10.1016\/j.cmpb.2023.107629_bib0038","unstructured":"F.J. Reh, Understanding pareto\u2019s principle-the 80-20 rule, 2017."},{"issue":"6","key":"10.1016\/j.cmpb.2023.107629_bib0039","doi-asserted-by":"crossref","first-page":"063019","DOI":"10.1117\/1.JEI.29.6.063019","article-title":"U-Net versus pix2pix: a comparative study on degraded document image binarization","volume":"29","author":"Basu","year":"2020","journal-title":"J. Electron. Imag."},{"issue":"5","key":"10.1016\/j.cmpb.2023.107629_bib0040","doi-asserted-by":"crossref","first-page":"229","DOI":"10.3390\/bioengineering9050229","article-title":"A deep learning approach for the morphological recognition of reactive lymphocytes in patients with COVID-19 infection","volume":"9","author":"Rodellar","year":"2022","journal-title":"Bioengineering"},{"issue":"3","key":"10.1016\/j.cmpb.2023.107629_bib0041","first-page":"364","article-title":"Convolutional neural network with data augmentation for SAR target recognition","volume":"13","author":"Ding","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10.1016\/j.cmpb.2023.107629_bib0042","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"13001","article-title":"Random erasing data augmentation","author":"Zhong","year":"2020"},{"key":"10.1016\/j.cmpb.2023.107629_bib0043","series-title":"2020 International Conference on Machine Vision and Image Processing (MVIP)","first-page":"1","article-title":"Pix2pix-based stain-to-stain translation: a solution for robust stain normalization in histopathology images analysis","author":"Salehi","year":"2020"},{"key":"10.1016\/j.cmpb.2023.107629_bib0044","article-title":"Unrolled generative adversarial networks","author":"Metz","year":"2016","journal-title":"arXiv preprint arXiv:1611.02163"},{"key":"10.1016\/j.cmpb.2023.107629_bib0045","doi-asserted-by":"crossref","first-page":"44247","DOI":"10.1109\/ACCESS.2019.2908991","article-title":"Nas-unet: neural architecture search for medical image segmentation","volume":"7","author":"Weng","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.cmpb.2023.107629_bib0046","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"6546","article-title":"Spatchgan: a statistical feature based discriminator for unsupervised image-to-image translation","author":"Shao","year":"2021"},{"key":"10.1016\/j.cmpb.2023.107629_bib0047","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.cmpb.2023.107629_bib0048","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"24","article-title":"Robust loss functions under label noise for deep neural networks","author":"Ghosh","year":"2017"},{"key":"10.1016\/j.cmpb.2023.107629_bib0049","article-title":"Unsupervised and semi-supervised learning with categorical generative adversarial networks","author":"Springenberg","year":"2015","journal-title":"arXiv preprint arXiv:1511.06390"},{"key":"10.1016\/j.cmpb.2023.107629_bib0050","article-title":"A disciplined approach to neural network hyper-parameters: part 1\u2013learning rate, batch size, momentum, and weight decay","author":"Smith","year":"2018","journal-title":"arXiv preprint arXiv:1803.09820"},{"key":"10.1016\/j.cmpb.2023.107629_bib0051","series-title":"Deep Learning for Coders with Fastai and PyTorch","author":"Howard","year":"2020"},{"key":"10.1016\/j.cmpb.2023.107629_bib0052","article-title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium","volume":"30","author":"Heusel","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.cmpb.2023.107629_bib0053","article-title":"A note on the inception score","author":"Barratt","year":"2018","journal-title":"arXiv preprint arXiv:1801.01973"},{"key":"10.1016\/j.cmpb.2023.107629_bib0054","series-title":"2018\u202fIEEE international conference on computational photography (ICCP)","first-page":"1","article-title":"Learned perceptual image enhancement","author":"Talebi","year":"2018"},{"key":"10.1016\/j.cmpb.2023.107629_bib0055","series-title":"Storage and Retrieval for Image and Video Databases III","first-page":"1","article-title":"Similariy of color images","author":"Stricker","year":"1995"},{"key":"10.1016\/j.cmpb.2023.107629_bib0056","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"1","article-title":"Going deeper with convolutions","author":"Szegedy","year":"2015"},{"key":"10.1016\/j.cmpb.2023.107629_bib0057","series-title":"Proceedings of the European conference on computer vision (ECCV)","first-page":"19","article-title":"Progressive neural architecture search","author":"Liu","year":"2018"},{"key":"10.1016\/j.cmpb.2023.107629_bib0058","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv preprint arXiv:1409.1556"},{"key":"10.1016\/j.cmpb.2023.107629_bib0059","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018"},{"key":"10.1016\/j.cmpb.2023.107629_bib0060","series-title":"2018\u202fIEEE\/ACM 26th International Symposium on Quality of Service (IWQoS)","first-page":"1","article-title":"Improved adam optimizer for deep neural networks","author":"Zhang","year":"2018"},{"key":"10.1016\/j.cmpb.2023.107629_sbref0057","series-title":"Facebook f8 conference","first-page":"1","article-title":"Decrappification, deoldification, and super resolution","author":"Antic","year":"2019"}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260723002948?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260723002948?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T00:55:57Z","timestamp":1722387357000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260723002948"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10]]},"references-count":61,"alternative-id":["S0169260723002948"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2023.107629","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2023,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Automatic normalized digital color staining in the recognition of abnormal blood cells using generative adversarial networks","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2023.107629","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"107629"}}