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New AI dataset released for breast cancer cytology analysis · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have released a comprehensive dataset for AI-assisted breast cancer cytology analysis, featuring 470 whole-slide images from 321 patients across multiple medical centers in India. The dataset includes over 7,000 annotated image patches with C1 to C5 reporting labels, stained using Papanicolaou or MayGrunwald Giemsa methods. This extensive collection, totaling approximately 950 GB, is made available through Zenodo, along with associated metadata and code for inspection and reuse. AI

IMPACT Provides a large-scale, multi-center dataset to advance AI-assisted diagnosis in breast cytology.

RANK_REASON The cluster describes the release of a new dataset for AI research, detailed in an arXiv paper.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New AI dataset released for breast cancer cytology analysis · 2 sources tracked

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Garima Jain, Abhijeet Patil, Surabhi Jain, Sanghamitra Pati, Amit Sethi, Sandeep Mathur, Pulkit Verma, Nishi Halduniya, Jatin Kashyap, Sharat Kumar, Simmi Kharb, Sunita Singh, Sucheta Devi Khuraijam, Sushma Khuraijam, Ratan Konjengbam, Arvind Kumar, Deep… ·

    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…

  2. arXiv cs.AI TIER_1 English(EN) · Nilam Adhav ·

    A Multi Center Breast FNAC Whole-Slide Cytology Dataset for AI-Assisted Patch-Wise Classification Using C1 to C5 Reporting Categories

    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 ce…

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