FEDERATED LUNG CANCER PREDICTION USING HISTOPATHOLOGICAL MEDICAL IMAGES

被引：1

作者：

Ayekai, Browne Judith ^{[1
]}

Chen Wenyu ^{[1
]}

Hailemichael, Mamo Tadiyos ^{[1
]}

Fiasam, Linda Delali ^{[3
]}

Kwaku, Agbesi Victor ^{[1
]}

Agbley, Fortune ^{[4
]}

Ayivi, Williams ^{[2
]}

Sam, Francis ^{[3
]}

Danso, Juliana Mantebea ^{[3
]}

Kulevome, Delanyo ^{[2
]}

Mawuli, Cobbinah Bernard ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China

[2] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China

[3] Univ Elect Sci & Technol China, Sch Informat & Software E, Chengdu 610054, Peoples R China

[4] Kwame Nkrumah Univ Sci & Technol, Dept English, Kumasi, Ghana

来源：

2022 19TH INTERNATIONAL COMPUTER CONFERENCE ON WAVELET ACTIVE MEDIA TECHNOLOGY AND INFORMATION PROCESSING (ICCWAMTIP) | 2022年

基金：

中国国家自然科学基金; 国家高技术研究发展计划(863计划);

关键词：

Federated learning; Medical imaging; Deep learning; Lung Cancer;

D O I：

10.1109/ICCWAMTIP56608.2022.10016519

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Machine learning is increasingly significant in health science because it can infer valuable information from high-dimensional data. However, combining research and patient data from various organizations and hospitals is frequently not practical due to privacy concerns. In this research, we conduct a study of federated learning for lung cancer prediction to demonstrate the effectiveness of collaborative and decentralized learning in a context where data is privacy preserved. We also conducted visual interpretation using GradCAM to validate the robust performance of the decentralized method in predicting lung cancer.

引用

页数：6

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