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<p>The rising incidence of oral cancer and associated poor prognosis, primarily due to delayed diagnosis, highlight the urgent need for artificial intelligence tools in clinical detection. However, efforts in this regard are hampered by the lack of large and ethnically heterogenous image datasets of oral lesions with clinically validated diagnoses. To address this gap, oral mucosa images captured with mobile device cameras were collected from cohorts spanning five countries. The images were systematically annotated with lesion type classifications as well as specific clinical diagnoses, then assessed for quality. The diagnoses were verified retrospectively by biopsy, where applicable, or by consensus verification by dental experts. The final dataset consists of 30,039 oral mucosa images supplemented by clinical metadata, made available on the MeMoSA Workbench platform. We believe that the MeMoSA dataset will serve as a significant resource to drive the training, evaluation, and refinement of AI-driven diagnostic algorithms, potentially improving diagnostic accuracy and enabling rigorous benchmarking against clinical expert assessments, for the early detection of oral cancer.</p>

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MeMoSA dataset: A multi-country collection of over 30,000 oral mucosa images with clinically labelled lesions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月7日

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  • Sara Chew,
  • Aliya Nabil,
  • Wei Jie Sin,
  • Davinna Satguna Rajah,
  • Hui Ying Lee,
  • Shin Hin Lau,
  • Chee Seng Chan,
  • Shier Nee Saw,
  • Ruwan Duminda Jayasinghe,
  • Jyotsna Rimal,
  • Rahmi Amtha,
  • Karthikeya Patil,
  • Wanninayake Mudiyanselage Tilakaratne,
  • Arvind Muthukrishnan,
  • Siti Mazlipah Ismail,
  • Zuraiza Mohamad Zaini,
  • Chuey Chuan Tan,
  • Yet Ching Goh,
  • Siew Wui Chan,
  • Nurul Izyan Zainuddin,
  • Muhammad Kamil Hassan,
  • Karthick Sekar,
  • Kathreena Kadir,
  • Nur Fauziani Zainul Abidin,
  • Nurshaline Pauline Hj Kipli,
  • Thaddius Herman Maling,
  • Alexander Ross Kerr,
  • Thomas George Kallarakkal,
  • Rosnah Binti Zain,
  • Senthilmani Rajendran,
  • Chee Sun Liew,
  • Sok Ching Cheong

摘要

The rising incidence of oral cancer and associated poor prognosis, primarily due to delayed diagnosis, highlight the urgent need for artificial intelligence tools in clinical detection. However, efforts in this regard are hampered by the lack of large and ethnically heterogenous image datasets of oral lesions with clinically validated diagnoses. To address this gap, oral mucosa images captured with mobile device cameras were collected from cohorts spanning five countries. The images were systematically annotated with lesion type classifications as well as specific clinical diagnoses, then assessed for quality. The diagnoses were verified retrospectively by biopsy, where applicable, or by consensus verification by dental experts. The final dataset consists of 30,039 oral mucosa images supplemented by clinical metadata, made available on the MeMoSA Workbench platform. We believe that the MeMoSA dataset will serve as a significant resource to drive the training, evaluation, and refinement of AI-driven diagnostic algorithms, potentially improving diagnostic accuracy and enabling rigorous benchmarking against clinical expert assessments, for the early detection of oral cancer.