MammosighTR：用于人工智能应用的BI-RADS注释的全国乳腺癌筛查乳房x线照片数据集。

IF 13.2 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Radiology-Artificial Intelligence Pub Date : 2025-08-13 DOI:10.1148/ryai.240841

Ural Koç, Emrah Karakaş, Ebru Akçapınar Sezer, Muhammed Said Beşler, Yaşar Alper Özkaya, Şehnaz Evrimler, Ahmet Yalçın, Hüseyin Alper Kızıloğlu, Uğur Kesimal, Meltem Oruç, İmran Çankaya, Duygu Koç Keleş, Neslihan Merd, Erdem Özkan, Numan İlteriş Çevik, Muhammet Batuhan Gökhan, Büşra Hayat, Mustafa Özer, Oğuzhan Tokur, Fatih Işık, Mehmet Alperen Tezcan, Muhammet Furkan Battal, Mecit Yüzkat, Nihat Barış Sebik, Fatih Karademir, Yasemin Topuz, Özgür Sezer, Songül Varlı, Erhan Akdoğan, Mustafa Mahir Ülgü, Şuayip Birinci

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引用次数: 0

摘要

“刚刚接受”的论文经过了全面的同行评审，并已被接受发表在《放射学：人工智能》杂志上。这篇文章将经过编辑，布局和校样审查，然后在其最终版本出版。请注意，在最终编辑文章的制作过程中，可能会发现可能影响内容的错误。MammosighTR数据集来源于 rkiye的国家乳腺癌筛查乳房x光检查项目，它提供了bi - rads标记的乳房x光照片，并详细注释了乳房成分和病变象限的位置，这可能有助于开发和测试乳腺癌检测中的人工智能模型。©RSNA, 2025年。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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MammosighTR: Nationwide Breast Cancer Screening Mammogram Dataset with BI-RADS Annotations for Artificial Intelligence Applications.

The MammosighTR dataset, derived from Türkiye's national breast cancer screening mammography program, provides BI-RADS-labeled mammograms with detailed annotations on breast composition and lesion quadrant location, which may be useful for developing and testing AI models in breast cancer detection. ©RSNA, 2025.

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来源期刊

Radiology-Artificial Intelligence

CiteScore

16.20

自引率

1.00%

发文量

期刊介绍： Radiology: Artificial Intelligence is a bi-monthly publication that focuses on the emerging applications of machine learning and artificial intelligence in the field of imaging across various disciplines. This journal is available online and accepts multiple manuscript types, including Original Research, Technical Developments, Data Resources, Review articles, Editorials, Letters to the Editor and Replies, Special Reports, and AI in Brief.