A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis

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 algor...

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Detalles Bibliográficos
Autores principales: Konz, Nicholas, Buda, Mateusz, Gu, Hanxue, Saha, Ashirbani, Yang, Jichen, Chłędowski, Jakub, Park, Jungkyu, Witowski, Jan, Geras, Krzysztof J., Shoshan, Yoel, Gilboa-Solomon, Flora, Khapun, Daniel, Ratner, Vadim, Barkan, Ella, Ozery-Flato, Michal, Martí, Robert, Omigbodun, Akinyinka, Marasinou, Chrysostomos, Nakhaei, Noor, Hsu, William, Sahu, Pranjal, Hossain, Md Belayat, Lee, Juhun, Santos, Carlos, Przelaskowski, Artur, Kalpathy-Cramer, Jayashree, Bearce, Benjamin, Cha, Kenny, Farahani, Keyvan, Petrick, Nicholas, Hadjiiski, Lubomir, Drukker, Karen, Armato, Samuel G., Mazurowski, Maciej A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2023
Materias:
Original Investigation
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9951043/
https://www.ncbi.nlm.nih.gov/pubmed/36821110
http://dx.doi.org/10.1001/jamanetworkopen.2023.0524

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9951043/
https://www.ncbi.nlm.nih.gov/pubmed/36821110
http://dx.doi.org/10.1001/jamanetworkopen.2023.0524

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