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<p>Oral squamous cell carcinoma is a major public health burden, particularly in regions with limited access to specialist pathology services. Oral brush cytology provides a minimally invasive approach for screening and early assessment, but the development of automated analysis methods requires well-annotated, multi-source datasets. We present a multicenter oral cytology dataset collected from tertiary medical centers in India. The dataset includes Papanicolaou- and May-Grünwald-Giemsa-stained whole-slide images, associated patient- and slide-level metadata, high-resolution image patches, and expert-verified nucleus-level annotations in QuPath-compatible GeoJSON format. The annotations support computational tasks including nucleus segmentation, instance segmentation, and cytological category classification. Dataset-level validation checks are provided to document file completeness, metadata consistency, annotation integrity, patch-to-slide linkage, and reuse readiness. The dataset is intended to facilitate development and benchmarking of computational pathology methods for oral cytology analysis.</p>

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A multi-center annotated oral cytology dataset for AI-assisted early detection of oral squamous cell carcinoma

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月30日

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  • Garima Jain,
  • Abhijeet Patil,
  • Poonam Goel,
  • Sanghamitra Pati,
  • Amit Sethi,
  • Gururaj Malekar,
  • Nilesh Kowe,
  • Nishi Halduniya,
  • Jatin Kashyap,
  • Divyajeet Rout,
  • Sharat Kumar,
  • Hitesh,
  • Heena Tabassum,
  • Rupinder Singh Dhaliwal,
  • Meeta Singh,
  • Ravi Meher,
  • Sucheta Devi Khuraijam,
  • Sushma Khuraijam,
  • Sharmila Laishram,
  • Simmi Kharb,
  • Sunita Singh,
  • K. Swaminadtan,
  • Ranjana Solanki,
  • Deepika Hemranjani,
  • Shashank Nath Singh,
  • Uma Handa,
  • Manveen Kaur,
  • Surinder Singhal,
  • Shivani Kalhan,
  • Rakesh Kumar Gupta,
  • S. Ravi,
  • D. Pavithra,
  • Sunil Kumar Mahto,
  • Arvind Kumar,
  • Deepali Tirkey,
  • Saurav Banerjee,
  • L. Sreelakshmi

摘要

Oral squamous cell carcinoma is a major public health burden, particularly in regions with limited access to specialist pathology services. Oral brush cytology provides a minimally invasive approach for screening and early assessment, but the development of automated analysis methods requires well-annotated, multi-source datasets. We present a multicenter oral cytology dataset collected from tertiary medical centers in India. The dataset includes Papanicolaou- and May-Grünwald-Giemsa-stained whole-slide images, associated patient- and slide-level metadata, high-resolution image patches, and expert-verified nucleus-level annotations in QuPath-compatible GeoJSON format. The annotations support computational tasks including nucleus segmentation, instance segmentation, and cytological category classification. Dataset-level validation checks are provided to document file completeness, metadata consistency, annotation integrity, patch-to-slide linkage, and reuse readiness. The dataset is intended to facilitate development and benchmarking of computational pathology methods for oral cytology analysis.