Strategies for integrating artificial intelligence into mammography screening programmes: a retrospective simulation analysis

Autor: Fisches, Zacharias V ^*, Ball, Michael ^*, Mukama, Trasias ^{*, *}, Štih, Vilim, Payne, Nicholas R, Hickman, Sarah E, Gilbert, Fiona J, Bunk, Stefan ^{†, **}, Leibig, Christian ^†

Publikováno v: In The Lancet Digital Health November 2024 6(11):e803-e814

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Evaluating Artificial Intelligence in Breast Cancer Screening

Autor: Hickman, Sarah

Evaluating Artificial Intelligence in Breast Cancer Screening Dr Sarah Elizabeth Hickman This thesis evaluates the application and performance of artificial intelligence (AI) in breast cancer screening. Breast cancer screening is conducted on a popul

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::fc1f42c4bb51cab46ec0a6e7551670e3

Federated learning for predicting clinical outcomes in patients with COVID-19

Autor: Dayan, Ittai, Roth, Holger R, Zhong, Aoxiao, Harouni, Ahmed, Gentili, Amilcare, Abidin, Anas Z, Liu, Andrew, Costa, Anthony Beardsworth, Wood, Bradford J, Tsai, Chien-Sung, Wang, Chih-Hung, Hsu, Chun-Nan, Lee, CK, Ruan, Peiying, Xu, Daguang, Wu, Dufan, Huang, Eddie, Kitamura, Felipe Campos, Lacey, Griffin, de Antônio Corradi, Gustavo César, Nino, Gustavo, Shin, Hao-Hsin, Obinata, Hirofumi, Ren, Hui, Crane, Jason C, Tetreault, Jesse, Guan, Jiahui, Garrett, John W, Kaggie, Joshua D, Park, Jung Gil, Dreyer, Keith, Juluru, Krishna, Kersten, Kristopher, Rockenbach, Marcio Aloisio Bezerra Cavalcanti, Linguraru, Marius George, Haider, Masoom A, AbdelMaseeh, Meena, Rieke, Nicola, Damasceno, Pablo F, E Silva, Pedro Mario Cruz, Wang, Pochuan, Xu, Sheng, Kawano, Shuichi, Sriswasdi, Sira, Park, Soo Young, Grist, Thomas M, Buch, Varun, Jantarabenjakul, Watsamon, Wang, Weichung, Tak, Won Young, Li, Xiang, Lin, Xihong, Kwon, Young Joon, Quraini, Abood, Feng, Andrew, Priest, Andrew N, Turkbey, Baris, Glicksberg, Benjamin, Bizzo, Bernardo, Kim, Byung Seok, Tor-Díez, Carlos, Lee, Chia-Cheng, Hsu, Chia-Jung, Lin, Chin, Lai, Chiu-Ling, Hess, Christopher P, Compas, Colin, Bhatia, Deepeksha, Oermann, Eric K, Leibovitz, Evan, Sasaki, Hisashi, Mori, Hitoshi, Yang, Isaac, Sohn, Jae Ho, Murthy, Krishna Nand Keshava, Fu, Li-Chen, de Mendonça, Matheus Ribeiro Furtado, Fralick, Mike, Kang, Min Kyu, Adil, Mohammad, Gangai, Natalie, Vateekul, Peerapon, Elnajjar, Pierre, Hickman, Sarah, Majumdar, Sharmila, McLeod, Shelley L, Reed, Sheridan, Gräf, Stefan, Harmon, Stephanie, Kodama, Tatsuya, Puthanakit, Thanyawee, Mazzulli, Tony, de Lavor, Vitor Lima, Rakvongthai, Yothin, Lee, Yu Rim, Wen, Yuhong, Gilbert, Fiona J, Flores, Mona G, Li, Quanzheng

Publikováno v: Nature medicine, vol 27, iss 10

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______325::cd17e39c003c4f4847e11b9ced64f109
https://escholarship.org/uc/item/6gc9c8js