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<p>Cutaneous spindle cell (CSC) lesions encompass a spectrum from benign to malignant neoplasms, often posing significant diagnostic challenges. Computer-aided diagnosis systems offer a promising solution to make pathologists’ decisions objective and faster. These systems usually require large-scale datasets with curated labels for effective training; however, manual annotation is time-consuming and expensive. To overcome this challenge, crowdsourcing has emerged as a popular and valuable strategy to scale up the labeling process by distributing the effort among different non-expert annotators. This work introduces AI4SkIN, the first public dataset Whole Slide Images (WSIs) for CSC neoplasms, annotated using an innovative crowdsourcing protocol. AI4SkIN dataset contains 641 Hematoxylin and Eosin stained WSIs with multiclass labels from both expert and trainee pathologists. The dataset improves CSC neoplasm diagnosis using advanced machine learning and crowdsourcing based on Gaussian Processes, showing that models trained on non-expert labels perform comparably to those using expert labels. In conclusion, we illustrate that AI4SkIN provides a good resource for developing and validating methods for multiclass CSC neoplasm classification.</p>

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A fusocelular skin dataset with whole slide images for deep learning models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月14日

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  • Rocío del Amor,
  • Miguel López-Pérez,
  • Pablo Meseguer,
  • Sandra Morales,
  • Liria Terradez,
  • Jose Aneiros-Fernandez,
  • Javier Mateos,
  • Rafael Molina,
  • Valery Naranjo

摘要

Cutaneous spindle cell (CSC) lesions encompass a spectrum from benign to malignant neoplasms, often posing significant diagnostic challenges. Computer-aided diagnosis systems offer a promising solution to make pathologists’ decisions objective and faster. These systems usually require large-scale datasets with curated labels for effective training; however, manual annotation is time-consuming and expensive. To overcome this challenge, crowdsourcing has emerged as a popular and valuable strategy to scale up the labeling process by distributing the effort among different non-expert annotators. This work introduces AI4SkIN, the first public dataset Whole Slide Images (WSIs) for CSC neoplasms, annotated using an innovative crowdsourcing protocol. AI4SkIN dataset contains 641 Hematoxylin and Eosin stained WSIs with multiclass labels from both expert and trainee pathologists. The dataset improves CSC neoplasm diagnosis using advanced machine learning and crowdsourcing based on Gaussian Processes, showing that models trained on non-expert labels perform comparably to those using expert labels. In conclusion, we illustrate that AI4SkIN provides a good resource for developing and validating methods for multiclass CSC neoplasm classification.