Federated learning enables big data for rare cancer boundary detection.
Federated learning enables big data for rare cancer boundary detection.
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DOI：
10.1038/s41467-022-33407-5
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发表时间：
2022-12-05
影响因子：
16.6
通讯作者：
中科院分区：
文献类型：
--
作者：
尽管机器学习(ML)已经显示出跨学科的前景，但样本外的泛化能力令人担忧。目前通过共享多站点数据来解决这一问题，但由于各种限制，这种集中化具有挑战性/无法扩展。联合ML(FL)通过只共享数值模型更新，为精确和可推广的ML提供了另一种范例。在这里，我们介绍了迄今为止最大的FL研究，涉及来自6大洲71个地点的数据，以生成针对罕见的胶质母细胞瘤疾病的自动肿瘤边界检测器，报告了文献中最大的此类数据集(n = 6， 314)。我们证明，与公共训练的模型相比，手术靶向肿瘤的分界改善了33%，完整肿瘤范围的分界改善了23%。我们预计我们的研究将：1)使更多的医疗保健研究通过大量不同的数据提供信息，确保对罕见疾病和代表性不足的人群得出有意义的结果；2)通过发布我们的共识模型，促进对胶质母细胞瘤的进一步分析；3)在这种规模和任务复杂性下展示FL的有效性，将其作为多站点合作的范式转换，从而减少对数据共享的需求。联合ML(FL)通过只共享数值模型更新，为训练准确和可推广的ML模型提供了另一种选择。在这里，作者提出了迄今为止最大的FL研究，以生成胶质母细胞瘤的自动肿瘤边界检测器。
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here, the authors present the largest FL study to-date to generate an automatic tumor boundary detector for glioblastoma.
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影响因子：
4.6
作者：
Adnan M;Kalra S;Cresswell JC;Taylor GW;Tizhoosh HR
通讯作者：
Tizhoosh HR
DOI：
10.1093/jamia/ocy017
发表时间：
2018-08-01
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Chang K;Balachandar N;Lam C;Yi D;Brown J;Beers A;Rosen B;Rubin DL;Kalpathy-Cramer J
通讯作者：
Kalpathy-Cramer J
影响因子：
82.9
作者：
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, C. K.;Ruan, Peiying;Xu, Daguang;Wu, Dufan;Huang, Eddie;Kitamura, Felipe Campos;Lacey, Griffin;de Antonio Corradi, Gustavo Cesar;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.;Silva, Pedro Mario Cruz E.;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-Diez, 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;Furtado de Mendonca, Matheus Ribeiro;Fralick, Mike;Kang, Min Kyu;Adil, Mohammad;Gangai, Natalie;Vateekul, Peerapon;Elnajjar, Pierre;Hickman, Sarah;Majumdar, Sharmila;McLeod, Shelley L.;Reed, Sheridan;Graf, 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
通讯作者：
Li, Quanzheng
影响因子：
2
作者：
Chaichana, Kaisorn L.;Pendleton, Courtney;Chambless, Lola;Camara-Quintana, Joaquin;Nathan, Jay K.;Hassam-Malani, Laila;Li, Gordon;Harsh, Griffith R.;Thompson, Reid C.;Lim, Michael;Quinones-Hinojos, Alfredo
通讯作者：
Quinones-Hinojos, Alfredo
影响因子：
2.4
作者：
Davatzikos, Christos;Rathore, Saima;Kontos, Despina
通讯作者：
Kontos, Despina