[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-371828-105":59,"doc-detail-371828-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","shifted-windowing-vision-transformer-based-skin-cancer-classification-via-transfer-learning","Shifted windowing vision transformer-based skin cancer classification via transfer learning","","Objective: Skin cancer is a major global health threat, and early identification of skin lesions can improve clinical decisions and reduce progression and metastasis. Limited and imbalanced training images, however, hinder model development. Methods: The work proposes a vision transformer with an attention mechanism to overcome CNN limitations in capturing global relationships, while applying transfer learning to enhance model parameters. Results: Experiments show superior performance versus traditional approaches. Conclusions: The approach is a potentially valuable tool for skin cancer identification.",{"@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/shifted-windowing-vision-transformer-based-skin-cancer-classification-via-transfer-learning/371828/",{"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/shifted-windowing-vision-transformer-based-skin-cancer-classification-via-transfer-learning/371828.png","ImageObject",300,407,{"name":92,"@type":93},"Kurz","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is early skin cancer detection important in this study?","Question",{"text":112,"@type":113},"Early identification of skin lesions supports clinical decision-making and improves treatment effectiveness, helping mitigate disease progression and metastasis.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What methods does the paper use to address limited and imbalanced training data?",{"text":117,"@type":113},"It introduces a vision transformer-based model with attention to capture global relationships, and uses transfer learning to strengthen the model’s weighting parameters.",{"name":119,"@type":110,"acceptedAnswer":120},"How do the experimental results compare to traditional approaches?",{"text":121,"@type":113},"The experiments indicate the proposed vision transformer approach achieves superior performance compared with traditional methods.","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},371828,1790621896,{"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":24,"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},2336478945635,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Clinics 80 (2025) 100724  \nContents lists available at ScienceDirect  \nClinics  \njournal [homepage:](homepage: www.elsevier.com/locate/clinsp)[ www.elsevier.com/locate/clinsp](homepage: www.elsevier.com/locate/clinsp)  \n| Original articles\u003Cbr>Shifted windowing vision transformer-based skin cancer classification via transfer learning |  |  | |\n| --- | --- | --- | --- |\n| Jian Lian a,* , Lina Han b , Xiaomei Wang b,* , Zhenpen Ji c, Lei Chengc\u003Cbr>a School of Intelligence Engineering, Shandong Management University, Jinan, Shandong, China\u003Cbr>b Department of Electrical and Automation, Shandong Labor Vocational and Technical College, Jinan, Shandong, China c Shandong Qinlu Energy Technology Co., Ltd, Jinan, 250357, China |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Skin cancer Classification Vision transformer Machine vision Transfer Learning |  | Objective: Skin cancer is widely recognized as one of the most perilous diseases on a global scale. Early identification of skin lesions can significantly enhance the treatment effects by aiding in clinical decision-making, hence mitigating the risk of disease progression and metastasis. Unfortunately, the skin images used for training are usually limited and imbalanced.\u003Cbr>Methods: To tackle the aforementioned challenges, a considerable number of deep learning models have been introduced for distinguishing skin cancer. And promising findings have been achieved by these algorithms. Nevertheless, the cross-domain adaptability and resilience of these models remain unresolved for actual clinical applications. One limitation of convolutional neural network-based deep models is their difficulty in finding the embeddings of global relationships for an image. This issue arises from the restricted receptive field of the convolutional operator, which primarily focuses on local information. To address this disparity, the present work introduces an innovative approach based on a vision transformer, with the aim of achieving skin cancer categorization. The presented model leverages the attention mechanism. Furthermore, transfer learning is employed to enhance the weighting parameters of the proposed model.\u003Cbr>Results: Experimental results show the superior performance of the proposed approach compared to traditional approaches, indicating the efficacy of the proposed vision transformer architecture.\u003Cbr>Conclusions: The promising results demonstrate that the proposed approach is a potentially valuable instrument for skin cancer identification. |  |\n\nIntroduction  \nSkin cancer is a highly widespread ailment worldwide, wherein melanoma stands as the leading cause of mortality among different types of cancers. Skin cancer is also a significant public health issue, with a higher incidence rate among Americans compared to all other types of carcinomas combined. On a daily basis, a staggering number of over 9500 individuals in the United States are diagnosed with skin cancer. According to a study conducted by Cancer Research Institute in 2023,1 the mortality rate for this particular disease is alarmingly high, with more than two individuals succumbing to it per hour. According to recent studies,1,2 newly diagnosed cases of malignant melanoma over the past decade. Although early detection indeed has the potential to decrease mortality rates. The timely detection of skin cancer is crucial, as it significantly improves the likelihood of successful treatment.3,4 The  \nsituation is exacerbated by a global shortage of radiologists and other healthcare professionals who possess the necessary qualifications to interpret diagnostic data, particularly in underprivileged regions and emerging economies.5 Furthermore, the presence of a dispute within a collective of radiologists serves to further substantiate this occurrence.6 Moreover, the significance of efficiency and effectiveness in life-saving endeavors necessitates the utilization of machine learning-based technologies that offer pr","cbCaiiO1g88gvpGb","https://ap.wps.com/l/cbCaiiO1g88gvpGb","pdf",6742712,"English","# Introduction\n# Materials and methods\n## Model design (vision transformer and attention)\n## Transfer learning strategy\n# Results\n# Conclusions","[{\"question\":\"Why is early skin cancer detection important in this study?\",\"answer\":\"Early identification of skin lesions supports clinical decision-making and improves treatment effectiveness, helping mitigate disease progression and metastasis.\"},{\"question\":\"What methods does the paper use to address limited and imbalanced training data?\",\"answer\":\"It introduces a vision transformer-based model with attention to capture global relationships, and uses transfer learning to strengthen the model’s weighting parameters.\"},{\"question\":\"How do the experimental results compare to traditional approaches?\",\"answer\":\"The experiments indicate the proposed vision transformer approach achieves superior performance compared with traditional methods.\"}]","Shifted windowing vision transformer-based skin cancer classification via transfer learning | PDF",1790192969]