HPFL: hyper-network guided personalized federated learning for multi-center tuberculosis chest x-ray diagnosis

被引:1
作者
Liu, Chang [1 ,2 ,3 ,4 ]
Luo, Yong [1 ,2 ,3 ,4 ]
Xu, Yongchao [1 ,2 ,3 ,4 ]
Du, Bo [1 ,2 ,3 ,4 ]
机构
[1] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China
[2] Wuhan Univ, Natl Engn Res Ctr Multimedia Software, Wuhan 430072, Peoples R China
[3] Wuhan Univ, Inst Artificial Intelligence, Wuhan 430072, Peoples R China
[4] Wuhan Univ, Hubei Key Lab Multimedia & Network Commun Engn, Wuhan 430072, Peoples R China
基金
中国国家自然科学基金;
关键词
Hyper-network; Federated learning; Tuberculosis; Chest x-ray; SEGMENTATION; FEATURES;
D O I
10.1007/s11042-023-17194-9
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Tuberculosis (TB) is one of the widespread infectious disease, and the early diagnosis and treatment can greatly improve the survival rate. Recently, machine learning has been introduced for assisting the diagnosis of TB, and to train a reliable diagnosis model, we need large amounts of data, which are often distributed in multiple medical centers. To protect the data privacy of different centers, we introduce federated learning (FL) in tuberculosis diagnosis. Since the data distributions of TB data vary significantly across different centers, we propose a personalized FL (PFL) method to explore the specific property of each client (i.e., medical center), and reduce its negative impacts from other clients. In particular, the contribution of each layer parameter is quantified by a hyper-network customized by the server for each client. Besides, a parameterization mechanism is introduced to update the hierarchical aggregation weights. To the best of our knowledge, this is the first PFL method for distributed TB diagnosis. Experimental results on several public datasets of chest X-ray images show that the proposed method significantly outperforms the state-of-the-art approaches in terms of both higher accuracy and faster convergence speed.
引用
收藏
页码:73273 / 73287
页数:15
相关论文
共 50 条
[11]  
Fallah A, 2020, ADV NEUR IN, V33
[12]  
Arivazhagan MG, 2019, Arxiv, DOI arXiv:1912.00818
[13]  
Ha D, 2016, Arxiv, DOI arXiv:1609.09106
[14]   Deep Residual Learning for Image Recognition [J].
He, Kaiming ;
Zhang, Xiangyu ;
Ren, Shaoqing ;
Sun, Jian .
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :770-778
[15]   A Novel Approach for Tuberculosis Screening Based on Deep Convolutional Neural Networks [J].
Hwang, Sangheum ;
Kim, Hyo-Eun ;
Jeong, Jihoon ;
Kim, Hee-Jin .
MEDICAL IMAGING 2016: COMPUTER-AIDED DIAGNOSIS, 2015, 9785
[16]   Two public chest X-ray datasets for computer-aided screening of pulmonary diseases [J].
Jaeger, Stefan ;
Candemir, Sema ;
Antani, Sameer ;
Wang, Yi-Xiang J. ;
Lu, Pu-Xuan ;
Thoma, George .
QUANTITATIVE IMAGING IN MEDICINE AND SURGERY, 2014, 4 (06) :475-477
[17]   Automatic Tuberculosis Screening Using Chest Radiographs [J].
Jaeger, Stefan ;
Karargyris, Alexandros ;
Candemir, Sema ;
Folio, Les ;
Siegelman, Jenifer ;
Callaghan, Fiona ;
Xue, Zhiyun ;
Palaniappan, Kannappan ;
Singh, Rahul K. ;
Antani, Sameer ;
Thoma, George ;
Wang, Yi-Xiang ;
Lu, Pu-Xuan ;
McDonald, Clement J. .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2014, 33 (02) :233-245
[18]   Advances and Open Problems in Federated Learning [J].
Kairouz, Peter ;
McMahan, H. Brendan ;
Avent, Brendan ;
Bellet, Aurelien ;
Bennis, Mehdi ;
Bhagoji, Arjun Nitin ;
Bonawitz, Kallista ;
Charles, Zachary ;
Cormode, Graham ;
Cummings, Rachel ;
D'Oliveira, Rafael G. L. ;
Eichner, Hubert ;
El Rouayheb, Salim ;
Evans, David ;
Gardner, Josh ;
Garrett, Zachary ;
Gascon, Adria ;
Ghazi, Badih ;
Gibbons, Phillip B. ;
Gruteser, Marco ;
Harchaoui, Zaid ;
He, Chaoyang ;
He, Lie ;
Huo, Zhouyuan ;
Hutchinson, Ben ;
Hsu, Justin ;
Jaggi, Martin ;
Javidi, Tara ;
Joshi, Gauri ;
Khodak, Mikhail ;
Konecny, Jakub ;
Korolova, Aleksandra ;
Koushanfar, Farinaz ;
Koyejo, Sanmi ;
Lepoint, Tancrede ;
Liu, Yang ;
Mittal, Prateek ;
Mohri, Mehryar ;
Nock, Richard ;
Ozgur, Ayfer ;
Pagh, Rasmus ;
Qi, Hang ;
Ramage, Daniel ;
Raskar, Ramesh ;
Raykova, Mariana ;
Song, Dawn ;
Song, Weikang ;
Stich, Sebastian U. ;
Sun, Ziteng ;
Suresh, Ananda Theertha .
FOUNDATIONS AND TRENDS IN MACHINE LEARNING, 2021, 14 (1-2) :1-210
[19]   Combination of texture and shape features to detect pulmonary abnormalities in digital chest X-rays [J].
Karargyris, Alexandros ;
Siegelman, Jenifer ;
Tzortzis, Dimitris ;
Jaeger, Stefan ;
Candemir, Sema ;
Xue, Zhiyun ;
Santosh, K. C. ;
Vajda, Szilard ;
Antani, Sameer ;
Folio, Les ;
Thoma, George R. .
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2016, 11 (01) :99-106
[20]  
Li T, 2021, PR MACH LEARN RES, V139