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<p>We announce the release of the OHSU MoleMapper Smartphone Skin Images dataset which contains over six years of new data acquired from the Oregon Health &amp; Science University’s (OHSU) MoleMapper study. This released dataset includes 27,499 mole images curated to exclude images with protected health identifiers, 7,305 images of skin patches near the mole images, 1,000 contextual images, and basic metadata from the participants. This data is available to qualified researchers on Sage Bionetwork’s Synapse platform under Synapse ID syn51520810 and represents the largest publicly available dataset of consumer-collected smartphone images of pigmented skin lesions. We release these data to the biomedical research community to enable quantitative analysis of images of non-clinician smartphone photography of skin lesions as well as to better understand what lesions appear concerning to the public. These data are unlabelled but in a machine learning context can be used to pre-train networks using self-supervised learning techniques or to quantitatively analyze the image quality of consumer-collected skin images.</p>

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New Release of User-Captured Images from the Oregon Health & Science University Melanoma MoleMapper Project

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月2日

查看全文
  • Tracy Petrie,
  • Ravikant Samatham,
  • Dan E. Webster,
  • Sancy A. Leachman

摘要

We announce the release of the OHSU MoleMapper Smartphone Skin Images dataset which contains over six years of new data acquired from the Oregon Health & Science University’s (OHSU) MoleMapper study. This released dataset includes 27,499 mole images curated to exclude images with protected health identifiers, 7,305 images of skin patches near the mole images, 1,000 contextual images, and basic metadata from the participants. This data is available to qualified researchers on Sage Bionetwork’s Synapse platform under Synapse ID syn51520810 and represents the largest publicly available dataset of consumer-collected smartphone images of pigmented skin lesions. We release these data to the biomedical research community to enable quantitative analysis of images of non-clinician smartphone photography of skin lesions as well as to better understand what lesions appear concerning to the public. These data are unlabelled but in a machine learning context can be used to pre-train networks using self-supervised learning techniques or to quantitatively analyze the image quality of consumer-collected skin images.