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Effective outcomes depend on early detection of pre-cancerous lesions, yet conventional cytopathology screening requires experts to manually interpret large numbers of cells, making it time-consuming, costly, and influenced by subjective experience. This work proposes a Multi-scale Window Transformer (MWT) for cytopathology image recognition using multi-scale window attention and an efficient convolutional feed-forward network, evaluated on multi-center datasets with improved performance.",{"@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/multi-scale-window-transformer-for-cervical-cytopathology-image-recognition/341135/",{"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/multi-scale-window-transformer-for-cervical-cytopathology-image-recognition/341135.png","ImageObject",300,407,{"name":92,"@type":93},"Aurora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-22",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},"Why is automated cervical cytopathology image recognition important?","Question",{"text":112,"@type":113},"Manual microscopic screening is labor-intensive and time-consuming, with accuracy affected by cytologists’ subjective experience. Automated recognition can reduce workload and improve diagnostic accuracy.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What is the key idea behind the proposed Multi-scale Window Transformer (MWT)?",{"text":117,"@type":113},"MWT uses multi-scale window multi-head self-attention to extract local details with small windows and integrate information across larger windows, enabling long-range feature integration without full-image self-attention.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the model represent features more efficiently for cytopathology images?",{"text":121,"@type":113},"The paper replaces the original MLP-based feed-forward network with convolutional feed-forward networks (CFFN), which are reported to be more efficient for cytopathology image representation.","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},341135,1790634885,{"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":39,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":46},4810365810221,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Computational and Structural Biotechnology Journal 24 (2024) 314–321  \nContents lists available at ScienceDirect  \nComputational and Structural Biotechnology Journal  \njournal [homepage:](homepage: www.elsevier.com/locate/csbj)[ www.elsevier.com/locate/csbj](homepage: www.elsevier.com/locate/csbj)  \n| Research Article\u003Cbr>Multi-scale window transformer for cervical cytopathology image recognition\u003Cbr>Jiaxiang Yi a, 1 , Xiuli Liu a, 1 , Shenghua Chengb,∗ , Li Chen c, Shaoqun Zeng a |  |  | |\n| --- | --- | --- | --- |\n| a Britton Chance Center and MoE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China\u003Cbr>b School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China c Department of Clinical Laboratory, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Cytopathology image recognition Multi-scale window transformer Convolutional feed-forward network Cervical cancer screening |  | Cervical cancer is a major global health issue, particularly in developing countries where access to healthcare is limited. Early detection of pre-cancerous lesions is crucial for successful treatment and reducing mortality rates. However, traditional screening and diagnostic processes require cytopathology doctors to manually interpret a huge number of cells, which is time-consuming, costly, and prone to human experiences. In this paper, we propose a Multi-scale Window Transformer (MWT) for cervical cytopathology image recognition. We design multi-scale window multi-head self-attention (MW-MSA) to simultaneously integrate cell features of different scales. Small window self-attention is used to extract local cell detail features, and large window self-attention aims to integrate features from smaller-scale window attention to achieve window-to-window information interaction. Our design enables long-range feature integration but avoids whole image self-attention (SA) in ViT or twice local window SA in Swin Transformer. We find convolutional feed-forward networks (CFFN) are more efficient than original MLP-based FFN for representing cytopathology images. Our overall model adopts a pyramid architecture. We establish two multi-center cervical cell classification datasets of two-category 192,123 images and four-category 174,138 images. Extensive experiments demonstrate that our MWT outperforms stateof-the-art general classification networks and specialized classifiers for cytopathology images in the internal and external test sets. The results on large-scale datasets prove the effectiveness and generalization of our proposed model. Our work provides a reliable cytopathology image recognition method and helps establish computeraided screening for cervical cancer. Our code is available at [https://github.com/nmyz669/MWT](https://github.com/nmyz669/MWT), and our web\u003Cbr>service tool can be accessed at [https://huggingface.co/spaces/nmyz/MWTdemo](https://huggingface.co/spaces/nmyz/MWTdemo). |  |\n\n1. Introduction  \nCervical cancer is the fourth most frequently diagnosed cancer, with an estimated 604,000 new cases and 342,000 deaths worldwide in 2020 [1]. The most commonly used cervical cancer screening method is the Papanicolaou (Pap) smear test, which involves examining cells collected from the cervix under a microscope. The widespread application of cytology screening in recent decades has been proven essential for the early detection and timely treatment of cervical cancer [2]. However, screening cervical smears under a microscope by cytologists consumesa significant amount of time and labor [3], and the accuracy of cervical  \n* Corresponding author.  \nE-mail address: [chengsh2023@smu.edu.cn](chengsh2023@smu.edu.cn) (S. Cheng).  \n1 These authors contributed equally to ","cbCaiskbLQEksIMn","https://ap.wps.com/l/cbCaiskbLQEksIMn","pdf",1534479,"English","# Introduction\n## Background on cervical cancer screening and cytopathology\n## Motivation for computer-aided image recognition\n## Prior deep learning approaches","[{\"question\":\"Why is automated cervical cytopathology image recognition important?\",\"answer\":\"Manual microscopic screening is labor-intensive and time-consuming, with accuracy affected by cytologists’ subjective experience. Automated recognition can reduce workload and improve diagnostic accuracy.\"},{\"question\":\"What is the key idea behind the proposed Multi-scale Window Transformer (MWT)?\",\"answer\":\"MWT uses multi-scale window multi-head self-attention to extract local details with small windows and integrate information across larger windows, enabling long-range feature integration without full-image self-attention.\"},{\"question\":\"How does the model represent features more efficiently for cytopathology images?\",\"answer\":\"The paper replaces the original MLP-based feed-forward network with convolutional feed-forward networks (CFFN), which are reported to be more efficient for cytopathology image representation.\"}]","Multi-scale window transformer for cervical cytopathology image recognition | PDF",1790040517]