[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-374807-105":59,"doc-detail-374807-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","a-longitudinal-dataset-of-tile-and-corresponding-dermoscopic-images-with-metadata-for-identifying-skin-cancers","A longitudinal dataset of tile and corresponding dermoscopic images with metadata for identifying skin cancers","","Machine learning classification algorithms support early detection of skin cancers, yet most approaches rely on single-image dermoscopic assessments and lack consistent contextual information. Skin monitoring contextual cues from multiple lesions across time are rarely available in existing datasets. This study presents a longitudinal dataset from 480 participants, including general and high-risk melanoma cohorts, with 250,162 3D total body imaging (tile) lesion images and corresponding dermoscopic images for 9,389 lesions, including longitudinal sequences for a subset of participants.",{"@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/a-longitudinal-dataset-of-tile-and-corresponding-dermoscopic-images-with-metadata-for-identifying-skin-cancers/374807/",{"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/a-longitudinal-dataset-of-tile-and-corresponding-dermoscopic-images-with-metadata-for-identifying-skin-cancers/374807.png","ImageObject",300,407,{"name":92,"@type":93},"4398046744996","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-23",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 do existing machine learning skin cancer algorithms perform limited contextual assessment?","Question",{"text":112,"@type":113},"Most algorithms are trained to judge malignancy from a single skin image, while clinicians combine information across multiple lesions and changes over time during examination.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What gaps does the presented dataset address?",{"text":117,"@type":113},"It targets scarcity and inconsistency of contextual information in skin image datasets and provides images of the same lesion across multiple time points and varying resolutions.",{"name":119,"@type":110,"acceptedAnswer":120},"What does the dataset include and how large is it?",{"text":121,"@type":113},"It includes 250,162 skin lesion images from 3D total body imaging (tile images) with corresponding dermoscopic images for 9,389 lesions, derived from skin monitoring of 480 study participants.","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},374807,1790577533,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":66,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":133,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":138,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":139,"faqs":140,"seo_title":141,"seo_description":67,"update_tm":142,"read_time":143},4398046744996,"[www. nature.com/scientificdata](www. nature.com/scientificdata)  \noPEN  \nData DESCriPtor  \na longitudinal dataset of tile and corresponding dermoscopic images with metadata for identifying skin cancers  \nNimaGhahari1 ✉, Liam Caffery1, Brigid Betz-Stablein2, adam Mothershaw 1,3, Dilki Jayasinghe1, Clare Primiero3, Shekhar S. Chandra4, Joachim torrano 3, H. Peter Soyer3,5 & Monika Janda1 ✉  \nMachine learning classification algorithms have emerged as promising tools to support the early detection of skin cancers. Existing algorithms typically assess malignancy of skin lesions based on a single skin image. this is in contrast with how clinicians integrate information from their physical examination, comparing multiple skin lesions of an individual and changes in lesions over time. Including contextual information could greatly enhance machine learning algorithms. However, contextual information in skin image datasets is predominantly scarce and inconsistent. additionally, a dataset containing images of the same lesion across multiple time points and varying resolutions is also lacking. To address these gaps, we present a comprehensive dataset derived from skin monitoring of 480 study participants recruited from a general population sample (n = 196) and a high-risk for melanoma cohort (n = 284). This dataset includes images of 250,162 skin lesions obtained from threedimensional total body imaging (tile images), along with corresponding dermoscopic images of 9,389 lesions. For 340 of the participants, longitudinal tile and dermoscopic images (ranging from 2 to 7) are provided.  \nBackground & Summary  \nSkin cancers emerge when there is a genetic mutation of skin cells resulting in uncontrolled proliferation, often in response to ultraviolet radiation1,2. Cutaneous melanoma (called melanoma from hereon) is the deadliest form of skin cancer responsible for 80% of skin cancer-related deaths. Basal cell carcinomas (BCCs) and squamous cell carcinomas (SCCs) are very common skin cancers, but less deadly than melanoma3. Although melanoma is relatively rare, its global incidence has increased over the past 50 years4,5. Some of this growth in incidence may have resulted from increased diagnosis scrutiny6. While late-stage skin cancers have a higher risk of mortality and require complex and costly treatment, early-stage diagnosis is associated with excellent survival and lower treatment costs7,8. Due to commonly being located on the skin surface, skin cancers are often visible and potentially detectable by looking at the skin9. Because of this visibility, various skin imaging technologies have been proposed to enhance early detection10. Machine learning algorithms, particularly neural networks, have shown potential in classifying skin images into benign and malignant lesions11. Most algorithms developed to date have only been trained on labelled dermoscopic (magnified) images11–14.  \nDermoscopic images are high-resolution, magnified images of skin lesions that allow clinicians to view deeper skin structures by reducing skin surface reflectivity15. These images are valuable in the differential diagnosis of melanoma and other skin cancers; however, their interpretation, even by trained clinicians is time-consuming, and results are highly dependent on the clinician’s experience 16. Although machine learning has great potential in classifying these skin images, training algorithms require a large number of accurately  \n1centre for Health Services Research, faculty of Medicine, the University of Queensland, Brisbane, Australia. 2Canfield Scientific, Parsippany, New Jersey, USA. 3frazer institute, Dermatology Research centre, the University of Queensland, Brisbane, Australia. 4School of electrical engineering and computer Science, the University of Queensland, Brisbane, Australia. 5 Dermatology Department, Princess Alexandra Hospital, Brisbane, Australia.✉ e-mail: [n.ghahari@uq.edu.au](n.ghahari@uq.edu.au); [m.janda@uq.edu.au](m.janda@uq.edu.au)  \n","cbCaimwT2HnOGGF6","https://ap.wps.com/l/cbCaimwT2HnOGGF6","pdf",1811423,"English","# Background & Summary\n## Rationale for contextual and longitudinal data\n## Dataset composition and participant cohorts","[{\"question\":\"Why do existing machine learning skin cancer algorithms perform limited contextual assessment?\",\"answer\":\"Most algorithms are trained to judge malignancy from a single skin image, while clinicians combine information across multiple lesions and changes over time during examination.\"},{\"question\":\"What gaps does the presented dataset address?\",\"answer\":\"It targets scarcity and inconsistency of contextual information in skin image datasets and provides images of the same lesion across multiple time points and varying resolutions.\"},{\"question\":\"What does the dataset include and how large is it?\",\"answer\":\"It includes 250,162 skin lesion images from 3D total body imaging (tile images) with corresponding dermoscopic images for 9,389 lesions, derived from skin monitoring of 480 study participants.\"}]","A longitudinal dataset of tile and corresponding dermoscopic images with metadata for identifying skin cancers | PDF",1790202459,23]