A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis.
Konz N, Buda M, Gu H, Saha A, Yang J, Chledowski J, Park J, Witowski J, Geras KJ, Shoshan Y, Gilboa-Solomon F, Khapun D, Ratner V, Barkan E, Ozery-Flato M, Martí R, Omigbodun A, Marasinou C, Nakhaei N, Hsu W, Sahu P, Hossain MB, Lee J, Santos C, Przelaskowski A, Kalpathy-Cramer J, Bearce B, Cha K, Farahani K, Petrick N, Hadjiiski L, Drukker K, Armato SG 3rd, Mazurowski MA.
Konz N, et al.
JAMA Netw Open. 2023 Feb 1;6(2):e230524. doi: 10.1001/jamanetworkopen.2023.0524.
JAMA Netw Open. 2023.
PMID: 36821110
Free PMC article.
The team with the highest mean sensitivity for biopsied lesions was the NYU B-Team, with 0.957 (95% CI, 0.924-0.984), and the second-place team, ZeDuS, had a mean sensitivity of 0.926 (95% CI, 0.881-0.964). ...
The team with the highest mean sensitivity for biopsied lesions was the NYU B-Team, with 0.957 (95% CI, 0.924-0.984), and the second- …