A Multi Center Breast FNAC Whole-Slide Cytology Dataset for AI-Assisted Patch-Wise Classification Using C1 to C5 Reporting Categories
arXiv:2606.30209v1 Announce Type: cross Abstract: We present a multi center breast fine needle aspiration cytology (FNAC) dataset designed for patch wise classification using C1 to C5 reporting labels. The prospective dataset includes 321 patients and 470 whole-slide images (WSIs) collected from participating tertiary medical centers in India between May 2023 and March 2026. Slides were stained using Papanicolaou (190 WSIs) or MayGrunwald Giemsa (280 WSIs), scanned on a Hamamatsu NanoZoomer S360 at 40X magnification and 0.25 microns per pixel, and stored directly in NDPI format. Across the 470
The proliferation of AI in medical imaging analysis coincides with the increasing availability of large-scale diagnostic datasets to train these models.
This development can significantly improve the accuracy and efficiency of breast cancer diagnosis, impacting healthcare systems and patient outcomes globally.
The availability of a multi-center dataset for breast FNAC allows for more robust training of AI models, potentially leading to widespread adoption of AI-assisted pathology.
- · Medical AI companies
- · Pathologists
- · Patients
- · Healthcare providers
- · Traditional diagnostic methods without AI integration
Improved early detection rates for breast cancer due to AI assistance.
Reduced workload for pathologists and potentially lower healthcare costs over time.
The establishment of standardized AI diagnostic platforms globally, leading to more equitable access to advanced diagnostics.
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Read at arXiv cs.AI