A Multi-Center Breast FNAC Cytology Dataset for AI-Assisted Patch-wise Classification Using C1-C5 Reporting Categories

Breast fine needle aspiration cytology (FNAC) is a rapid, minimally invasive, and cost-effective method for evaluating breast lesions. Despite its clinical utility, FNAC interpretation remains dependent on expert cytopathological review and can be affected by variation in smear preparation, staining, cellularity, and overlapping cytomorphological features. Development of reliable artificial intelligence (AI)-assisted cytology systems requires large, diverse, and well-annotated datasets. We present a multi-center breast FNAC cytology dataset for AI-assisted patch-wise classification using C1--C5 reporting categories. The dataset was collected prospectively from participating tertiary medical centers in India between May 2023 and March 2026, with ethical approval from the participating centers and the Indian Council of Medical Research (ICMR). It contains 321 patients and 470 whole slide images (WSIs), including 190 Papanicolaou (PAP)-stained and 280 May--Grunwald--Giemsa (MGG)-stained slides. The WSIs were scanned using a Hamamatsu whole slide scanner at 40$\times$ magnification and 0.25 microns per pixel. The dataset includes 7,393 manually extracted diagnostically relevant image patches labeled as C1 insufficient, C2 benign, C3 atypical, C4 suspicious for malignancy, or C5 malignant. Patch labels were assigned by a pathologist and verified by a senior pathologist. The released data will include WSIs, GeoJSON annotation files with patch coordinates and C1--C5 labels, extracted patch images, anonymised patient-level metadata, and patch-level metadata. The complete dataset is approximately 950 GB and will be made available as an open-access resource. This dataset provides a resource for developing and benchmarking AI methods for breast cytology classification and future slide-level decision-support workflows. WSIs are also available for download.Set 1 (26 WSIs): https://zenodo.org/record/20701935 Set 2 (19 WSIs): https://zenodo.org/record/20701937 Set 3 (25 WSIs): https://zenodo.org/record/20701939 Set 4 (22 WSIs): https://zenodo.org/record/20701943 Set 5 (18 WSIs): https://zenodo.org/record/20701945 Set 6 (17 WSIs): https://zenodo.org/record/20701947 Set 7 (28 WSIs): https://zenodo.org/record/20701949 Set 8 (26 WSIs): https://zenodo.org/record/20701951Set 9 (36 WSIs): https://zenodo.org/record/20701953 Set 10 (24 WSIs): https://zenodo.org/record/20701955 Set 11 (26 WSIs): https://zenodo.org/record/20701958 Set 12 (21 WSIs): https://zenodo.org/record/20701960 Set 13 (18 WSIs): https://zenodo.org/record/20701962 Set 14 (20 WSIs): https://zenodo.org/record/20701964 Set 15 (18 WSIs): https://zenodo.org/record/20701966 Set 16 (18 WSIs): https://zenodo.org/record/20701968 Set 17 (28 WSIs): https://zenodo.org/record/20701970 Set 18 (20 WSIs): https://zenodo.org/record/20701972 Set 19 (26 WSIs): https://zenodo.org/record/20701976 Set 20 (28 WSIs): https://zenodo.org/record/20701978 Set 21 (6 WSIs): https://zenodo.org/record/20701980