{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T22:30:02Z","timestamp":1793226602134,"version":"4.5.0"},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004622","name":"Dutch Cancer Society","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004622","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100023452","name":"Hanarth Fund Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100023452","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008375","name":"University of Basel","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008375","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Pathology Informatics"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.jpi.2026.100702","type":"journal-article","created":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T06:26:27Z","timestamp":1785565587000},"page":"100702","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["A protocol for evaluating robustness to H&E staining variation in computational pathology models"],"prefix":"10.1016","volume":"22","author":[{"given":"Lydia A.","family":"Schoenpflug","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikki J.","family":"van den Berg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sonali","family":"Andani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nanda","family":"Horeweg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jurriaan","family":"Barkey Wolf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tjalling","family":"Bosse","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Viktor","family":"Koelzera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maxime W.","family":"Lafarge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jpi.2026.100702_bb0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpi.2025.100423","article-title":"An international study of stain variability in histopathology using qualitative and quantitative analysis","volume":"17","author":"Dunn","year":"2025","journal-title":"J Pathol Inform"},{"key":"10.1016\/j.jpi.2026.100702_bb0050","series-title":"Medical Imaging 2019: Digital Pathology","first-page":"38","article-title":"Fragile neural networks: the importance of image standardization for deep learning in digital pathology","author":"Folmsbee","year":"2019"},{"key":"10.1016\/j.jpi.2026.100702_bb0060","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-021-24698-1","article-title":"The impact of site-specific digital histology signatures on deep learning model accuracy and bias","volume":"12","author":"Howard","year":"2021","journal-title":"Nat Commun"},{"key":"10.1016\/j.jpi.2026.100702_bb0095","article-title":"Impact of stain variation and color normalization for prognostic predictions in pathology","volume":"15","author":"Lin","year":"2025","journal-title":"Sci Rep"},{"key":"10.1016\/j.jpi.2026.100702_bb0180","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1109\/JBHI.2020.3032060","article-title":"Measuring domain shift for deep learning in histopathology","volume":"25","author":"Stacke","year":"2021","journal-title":"IEEE J Biomed Health Inform"},{"key":"10.1016\/j.jpi.2026.100702_bb0190","doi-asserted-by":"crossref","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.jpi.2026.100702_bb0040","article-title":"Deeply supervised two stage generative adversarial network for stain normalization","volume":"15","author":"Du","year":"2025","journal-title":"Sci Rep"},{"key":"10.1016\/j.jpi.2026.100702_bb0065","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.compmedimag.2016.05.003","article-title":"Stain normalization using sparse autoencoders (stanosa): application to digital pathology","volume":"57","author":"Janowczyk","year":"2017","journal-title":"Comput Med Imaging Graph"},{"key":"10.1016\/j.jpi.2026.100702_bb0115","series-title":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","first-page":"1107","article-title":"A method for normalizing histology slides for quantitative analysis","author":"Macenko","year":"2009"},{"key":"10.1016\/j.jpi.2026.100702_bb0170","series-title":"Proceedings of the 10th IRC Conference on Science, Engineering and Technology","first-page":"323","article-title":"Stainnorm: A lightweight and robust network for stain colour normalization in histopathology","author":"Sim","year":"2025"},{"key":"10.1016\/j.jpi.2026.100702_bb0005","series-title":"Medical Imaging 2018: Digital Pathology","first-page":"34","article-title":"H&E stain augmentation improves generalization of convolutional networks for histopathological mitosis detection","author":"Balkenhol","year":"2018"},{"key":"10.1016\/j.jpi.2026.100702_bb0010","doi-asserted-by":"crossref","first-page":"792","DOI":"10.1109\/TMI.2017.2781228","article-title":"Adversarial stain transfer for histopathology image analysis","volume":"37","author":"Bentaieb","year":"2018","journal-title":"IEEE Trans Med Imaging"},{"key":"10.1016\/j.jpi.2026.100702_bb0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpi.2022.100183","article-title":"Data-driven color augmentation for H&E stained images in computational pathology","volume":"14","author":"Marini","year":"2023","journal-title":"J Pathol Inform"},{"key":"10.1016\/j.jpi.2026.100702_bb0145","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","first-page":"330","article-title":"Histopathological stain transfer using style transfer network with adversarial loss","author":"Nishar","year":"2020"},{"key":"10.1016\/j.jpi.2026.100702_bb0165","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022","first-page":"212","article-title":"Randstainna: learning stain-agnostic features from histology slides by bridging stain augmentation and normalization","author":"Shen","year":"2022"},{"key":"10.1016\/j.jpi.2026.100702_bb0090","series-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","first-page":"83","article-title":"Domain-adversarial neural networks to address the appearance variability of histopathology images","author":"Lafarge","year":"2017"},{"key":"10.1016\/j.jpi.2026.100702_bb0155","article-title":"Staining invariant features for improving generalization of deep convolutional neural networks in computational pathology","volume":"7-2019","author":"Ot\u00e1lora","year":"2019","journal-title":"Front Bioeng Biotechnol"},{"key":"10.1016\/j.jpi.2026.100702_bb0225","series-title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","first-page":"10751","article-title":"A survey of pathology foundation model: Progress and future directions","author":"Xiong","year":"2025"},{"key":"10.1016\/j.jpi.2026.100702_bb0220","series-title":"Computer Vision \u2013 ECCV 2024 Workshops","first-page":"68","article-title":"A good feature extractor is all you need for weakly supervised pathology slide classification","author":"W\u00f6lflein","year":"2025"},{"key":"10.1016\/j.jpi.2026.100702_bb0070","author":"de Jong"},{"key":"10.1016\/j.jpi.2026.100702_bb0080","author":"K\u00f6men"},{"key":"10.1016\/j.jpi.2026.100702_bb0085","series-title":"Advancements In Medical Foundation Models: Explainability, Robustness, Security, and Beyond","article-title":"Do histopathological foundation models eliminate batch effects? a comparative study","author":"K\u00f6men","year":"2024"},{"key":"10.1016\/j.jpi.2026.100702_bb0100","author":"Lin"},{"key":"10.1016\/j.jpi.2026.100702_bb0055","doi-asserted-by":"crossref","first-page":"3312","DOI":"10.1093\/bioinformatics\/btac315","article-title":"Reet: robustness evaluation and enhancement toolbox for computational pathology","volume":"38","author":"Foote","year":"2022","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jpi.2026.100702_bb0160","doi-asserted-by":"crossref","first-page":"2098","DOI":"10.1038\/s41379-021-00859-x","article-title":"Quality control stress test for deep learning-based diagnostic model in digital pathology","volume":"34","author":"Sch\u00f6mig-Markiefka","year":"2021","journal-title":"Mod Pathol"},{"key":"10.1016\/j.jpi.2026.100702_bb0195","series-title":"Machine Learning in Medical Imaging","first-page":"249","article-title":"Nuclear segmentation and classification: On color and compression generalization","author":"Vu","year":"2022"},{"key":"10.1016\/j.jpi.2026.100702_bb0205","doi-asserted-by":"crossref","first-page":"54","DOI":"10.4103\/jpi.jpi_6_21","article-title":"Stress testing pathology models with generated artifacts","volume":"12","author":"Wang","year":"2021","journal-title":"J Pathol Inform"},{"key":"10.1016\/j.jpi.2026.100702_bb0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102809","article-title":"From modern CNNs to vision transformers: assessing the performance, robustness, and classification strategies of deep learning models in histopathology","volume":"87","author":"Springenberg","year":"2023","journal-title":"Med Image Anal"},{"key":"10.1016\/j.jpi.2026.100702_bb0030","doi-asserted-by":"crossref","DOI":"10.1002\/2056-4538.70080","article-title":"Impact of tissue staining and scanner variation on the performance of pathology foundation models: a study of sarcomas and their mimics","volume":"12","author":"Chai","year":"2026","journal-title":"J Pathol Clin Res"},{"key":"10.1016\/j.jpi.2026.100702_bb0125","article-title":"CNN stability training improves robustness to scanner and IHC-based image variability for epithelium segmentation in cervical histology","volume":"10-2023","author":"Miranda Ruiz","year":"2023","journal-title":"Front Med"},{"key":"10.1016\/j.jpi.2026.100702_bb0185","series-title":"ICASSP 2023\u20132023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"1","article-title":"Assessing the robustness of deep learning-assisted pathological image analysis under practical variables of imaging system","author":"Sun","year":"2023"},{"key":"10.1016\/j.jpi.2026.100702_bb0150","doi-asserted-by":"crossref","DOI":"10.1038\/s41597-024-03122-5","article-title":"Registered multi-device\/staining histology image dataset for domain-agnostic machine learning models","volume":"11","author":"Ochi","year":"2024","journal-title":"Sci Data"},{"key":"10.1016\/j.jpi.2026.100702_bb0025","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1200\/PO.17.00073","article-title":"Landscape of microsatellite instability across 39 cancer types","author":"Bonneville","year":"2017","journal-title":"JCO Precis Oncol"},{"key":"10.1016\/j.jpi.2026.100702_bb0075","doi-asserted-by":"crossref","first-page":"7452","DOI":"10.18632\/oncotarget.13918","article-title":"Performance evaluation for rapid detection of pan-cancer microsatellite instability with mantis","volume":"8","author":"Kautto","year":"2016","journal-title":"Oncotarget"},{"key":"10.1016\/j.jpi.2026.100702_bb0135","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1038\/nature11252","article-title":"Comprehensive molecular characterization of human colon and rectal cancer","volume":"487","author":"Network","year":"2012","journal-title":"Nature"},{"key":"10.1016\/j.jpi.2026.100702_bb0130","doi-asserted-by":"crossref","DOI":"10.1093\/gigascience\/giaf086","article-title":"Surgen: 1020 H&E-stained whole-slide images with survival and genetic markers","volume":"14","author":"Myles","year":"2025","journal-title":"Gigascience"},{"key":"10.1016\/j.jpi.2026.100702_bb0020","doi-asserted-by":"crossref","DOI":"10.7717\/peerj.8242","article-title":"Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks","volume":"7","author":"B\u00e1ndi","year":"2019","journal-title":"PeerJ"},{"key":"10.1016\/j.jpi.2026.100702_bb0035","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1038\/s41591-024-02857-3","article-title":"Towards a general-purpose foundation model for computational pathology","volume":"30","author":"Chen","year":"2024","journal-title":"Nat Med"},{"key":"10.1016\/j.jpi.2026.100702_bb0015","author":"Bioptimus"},{"key":"10.1016\/j.jpi.2026.100702_bb0230","author":"Zimmermann"},{"key":"10.1016\/j.jpi.2026.100702_bb0110","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","article-title":"Data-efficient and weakly supervised computational pathology on whole-slide images","volume":"5","author":"Lu","year":"2021","journal-title":"Nat Biomed Eng"},{"key":"10.1016\/j.jpi.2026.100702_bb0105","series-title":"International Conference on Learning Representations","article-title":"Decoupled weight decay regularization","author":"Loshchilov","year":"2019"},{"key":"10.1016\/j.jpi.2026.100702_bb0200","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1016\/j.ccell.2023.08.002","article-title":"Transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study","volume":"41","author":"Wagner","year":"2023","journal-title":"Cancer Cell"},{"key":"10.1016\/j.jpi.2026.100702_bb0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102559","article-title":"Transformer-based unsupervised contrastive learning for histopathological image classification","volume":"81","author":"Wang","year":"2022","journal-title":"Med Image Anal"},{"key":"10.1016\/j.jpi.2026.100702_bb0140","article-title":"Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: a retrospective multi-centric study","volume":"4","author":"Niehues","year":"2023","journal-title":"Cell Rep Med"},{"key":"10.1016\/j.jpi.2026.100702_bb0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102645","article-title":"Retccl: clustering-guided contrastive learning for whole-slide image retrieval","volume":"83","author":"Wang","year":"2023","journal-title":"Med Image Anal"}],"container-title":["Journal of Pathology Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2153353926001628?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2153353926001628?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T20:16:05Z","timestamp":1788552965000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2153353926001628"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":46,"alternative-id":["S2153353926001628"],"URL":"https:\/\/doi.org\/10.1016\/j.jpi.2026.100702","relation":{},"ISSN":["2153-3539"],"issn-type":[{"value":"2153-3539","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A protocol for evaluating robustness to H&E staining variation in computational pathology models","name":"articletitle","label":"Article Title"},{"value":"Journal of Pathology Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jpi.2026.100702","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Inc. on behalf of Association for Pathology Informatics.","name":"copyright","label":"Copyright"}],"article-number":"100702"}}