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Histopathological image analysis is essential for cancer diagnosis, yet it is limited by human error and biological and technical variability. The study presents HistopathAI, a hybrid histopathology classification framework combining supervised contrastive learning with hybrid deep feature fusion and cross-entropy to improve precision, especially under imbalanced data.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/generalized-deep-learning-for-histopathology-image-classification-using-supervised-contrastive-learning/338748/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/generalized-deep-learning-for-histopathology-image-classification-using-supervised-contrastive-learning/338748.png","ImageObject",300,407,{"name":92,"@type":93},"Ophelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does HistopathAI target in histopathology image classification?","Question",{"text":112,"@type":113},"It targets improved classification accuracy for histopathology images, particularly when data are imbalanced and when variability can affect diagnostic reliability.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does HistopathAI combine supervised contrastive learning with feature fusion?",{"text":117,"@type":113},"It uses supervised contrastive learning for feature representation and employs hybrid deep feature fusion to merge feature representations from EfficientNetB3 and ResNet50.",{"name":119,"@type":110,"acceptedAnswer":120},"What outcomes does the study report for HistopathAI’s performance?",{"text":121,"@type":113},"HistopathAI achieves state-of-the-art classification accuracy across multiple datasets, with improved results in both binary and multiclass tasks confirmed through statistical testing.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},338748,1790292345,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":36},7971461741311,"https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826","Journal of Advanced Research 75 (2025) 389–404  \nContents lists available at ScienceDirect  \nJournal of Advanced Research  \njournal [homepage: www. elsevier. com/locate/jare](homepage: www. elsevier. com/locate/jare)  \nOriginal Manuscript  \nGeneralized deep learning for histopathology image classification using supervised contrastive learning  \nMd Mamunur Rahaman a, Ewan K.A. Millar b,c,d, Erik Meijering a, ⇑  \na School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia b Department of Anatomical Pathology, NSW Health Pathology, St. George Hospital, Sydney NSW 2217, Australia c St. George and Sutherland Clinical School, University of New South Wales, Sydney NSW 2052, Australia d Faculty of Medicine & Health Sciences, Western Sydney University, Sydney NSW 2560, Australia  \nh i g h l i g h t s  \n Introduction of HistopathAI, a novel hybrid network structure that merges SCL strategies, specifically tailored for imbalanced data, with a CE loss function to bolster histopathology image classification.  \n Introduction of the HDFF technique that amalgamates feature vectors from both EfficientNetB3 and ResNet50, yielding a more comprehensive representation of histopathology images.  \n Implementation of a stepwise methodology that transitions from feature learning to classifier learning, resonating with the foundational concept of contrastive learning torefine the normalized embedding space.  \n Comprehensive training and testing of our model for diverse histopathology domains, utilizing seven publicly accessible datasets along with one private dataset, resulting in state-of-the-art performance and demonstrating the generalizability of our framework.  \ng r a p h i c a l a b s t r a c t  \na r t i c l e i n f o  \nArticle history:  \nReceived 19 June 2024  \nRevised 7 October 2024  \nAccepted 10 November 2024  \nAvailable online 16 November 2024  \na b s t r a c t  \nIntroduction: Cancer is a leading cause of death worldwide, necessitating effective diagnostic tools for early detection and treatment. Histopathological image analysis is crucial for cancer diagnosis but is often hindered by human error and variability. This study introduces HistopathAI, a hybrid network designed for histopathology image classification, aimed at enhancing diagnostic precision and efficiency in clinical pathology.  \nq Peer review under responsibility of Cairo University.  \n⇑ Corresponding author.  \nE-mail [addresses:](addresses: md_mamunur.rahaman@unsw.edu.au)[ md_mamunur.rahaman@unsw.edu.au](addresses: md_mamunur.rahaman@unsw.edu.au) (M.M. Rahaman), [ewan.millar@health.nsw.gov.au](ewan.millar@health.nsw.gov.au) (E.K.A. Millar), [erik.meijering@unsw.edu.au](erik.meijering@unsw.edu.au) (E. Meijering).  \n[https://doi.org/10.1016/j.jare.2024.11.013](https://doi.org/10.1016/j.jare.2024.11.013)  \n2090-1232/􀀁 2024 Published by Elsevier B.V. on behalf of Cairo University.  \nThis is an open access article under the CC BY-NC-ND license ([http://creativecommons.org/licenses/by-nc-nd/4.0/](http://creativecommons.org/licenses/by-nc-nd/4.0/)).  \nM.M. Rahaman, Ewan K.A. Millar and E. Meijering Journal of Advanced Research 75 (2025) 389–404  \nKeywords:  \nHistopathological image analysis Contrastive learning Imbalanced data  \nHybrid deep feature fusion Feature representation  \nCancer diagnosis  \nObjectives: The primary goal of this study is to demonstrate that HistopathAI, leveraging supervised contrastive learning (SCL) and hybrid deep feature fusion (HDFF), can significantly improve the accuracy of histopathological image classification, including scenarios involving imbalanced datasets.  \nMethods: HistopathAI integrates features from EfficientNetB3 and ResNet50, using HDFF to provide a rich representation of histopathology images. The framework employs a sequential methodology, transitioning from feature learning to classifier learning, mirroring the essence of contrastive learning with the aim of producing superior feature representations. The mo","cbCaic0r0B6DyRq1","https://ap.wps.com/l/cbCaic0r0B6DyRq1","pdf",2066026,16,"English","# Introduction\n# Objectives\n# Methods\n# Results\n# Conclusion","[{\"question\":\"What problem does HistopathAI target in histopathology image classification?\",\"answer\":\"It targets improved classification accuracy for histopathology images, particularly when data are imbalanced and when variability can affect diagnostic reliability.\"},{\"question\":\"How does HistopathAI combine supervised contrastive learning with feature fusion?\",\"answer\":\"It uses supervised contrastive learning for feature representation and employs hybrid deep feature fusion to merge feature representations from EfficientNetB3 and ResNet50.\"},{\"question\":\"What outcomes does the study report for HistopathAI’s performance?\",\"answer\":\"HistopathAI achieves state-of-the-art classification accuracy across multiple datasets, with improved results in both binary and multiclass tasks confirmed through statistical testing.\"}]","Generalized deep learning for histopathology image classification using supervised contrastive learning | PDF",1790025443]