作者
Nicholas Konz, Mateusz Buda, Hanxue Gu, Ashirbani Saha, Jichen Yang, Jakub Chłędowski, Jungkyu Park, Jan Witowski, Krzysztof J Geras, Yoel Shoshan, Flora Gilboa-Solomon, Daniel Khapun, Vadim Ratner, Ella Barkan, Michal Ozery-Flato, Robert Martí, Akinyinka Omigbodun, Chrysostomos Marasinou, Noor Nakhaei, William Hsu, Pranjal Sahu, Md Belayat Hossain, Juhun Lee, Carlos Santos, Artur Przelaskowski, Jayashree Kalpathy-Cramer, Benjamin Bearce, Kenny Cha, Keyvan Farahani, Nicholas Petrick, Lubomir Hadjiiski, Karen Drukker, Samuel G Armato, Maciej A Mazurowski
发表日期
2023/2/1
期刊
JAMA network open
卷号
6
期号
2
页码范围
e230524-e230524
出版商
American Medical Association
简介
Importance
An accurate and robust artificial intelligence (AI) algorithm for detecting cancer in digital breast tomosynthesis (DBT) could significantly improve detection accuracy and reduce health care costs worldwide.
Objectives
To make training and evaluation data for the development of AI algorithms for DBT analysis available, to develop well-defined benchmarks, and to create publicly available code for existing methods.
Design, Setting, and Participants
This diagnostic study is based on a multi-institutional international grand challenge in which research teams developed algorithms to detect lesions in DBT. A data set of 22 032 reconstructed DBT volumes was made available to research teams. Phase 1, in which teams were provided 700 scans from the training set, 120 from the validation set, and 180 from the test set, took place from December 2020 to January 2021, and phase 2, in which teams were given the …
学术搜索中的文章
N Konz, M Buda, H Gu, A Saha, J Yang, J Chłędowski… - JAMA network open, 2023