{"paper":{"title":"A Multi Center Breast FNAC Whole-Slide Cytology Dataset for AI-Assisted Patch-Wise Classification Using C1 to C5 Reporting Categories","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Abhijeet Patil, Amit Sethi, Arvind Kumar, Basumitra Das, B. G. Malathi, Biswajit Dey, Deepali Tirkey, Deepika Hemranjani, Garima Jain, Indu R. Nair, Jatin Kashyap, Manveen Kaur, Nilam Adhav, Niraj Kumari, Nishi Halduniya, Pulkit Verma, Rakesh Kumar Gupta, Ranjana Solanki, Ratan Konjengbam, Ravindra Karle, Sandeep Mathur, Sanghamitra Pati, Saurav Banerjee, Sharat Kumar, Shashank Nath Singh, Shivani Kalhan, Shruti Gupta, Simmi Kharb, Sucheta Devi Khuraijam, Sunil Kumar Komanapalli, Sunita Singh, Surabhi Jain, Sushma Khuraijam, Tanaya Kulkarni, Uma Handa, Vaishali Gaikwad, Vandana Raphael, Vidya C., Yogender P.","submitted_at":"2026-06-29T12:24:50Z","abstract_excerpt":"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 WSIs, 446 WSIs contain annotated patch regions, y"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.30209","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.30209/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}