An automatic annotation method based on federated learning

Bochao Ao; Bingbing Fan

doi:10.1117/12.2674584

28 April 2023 An automatic annotation method based on federated learning

Bochao Ao, Bingbing Fan

Proceedings Volume 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022); 126260I (2023) https://doi.org/10.1117/12.2674584
Event: International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 2022, Zhuhai, China

Abstract

With the explosive growth of neoconiosis worldwide, medical institutions worldwide generate a large number of chest radiograph images to be annotated for neoconiosis diagnosis every day. Moreover, due to the privacy of patient information, it is not possible to pool medical data from multiple medical institutions to jointly build a fast and accurate medical image annotation system for the task of new coronary pneumonia annotation. To this end, we propose a federal learning-based automatic annotation method that enables multiple medical institutions to design and develop an accurate and robust annotation system for neocoronary pneumonia without sharing data. Experimental results show that the proposed federated learning-based automatic annotation method (93.37% accuracy) is able to protect the privacy of medical image data across hospitals and accomplish a higher accuracy rate of medical image annotation compared to the automatic annotation method constructed by aggregating data (95.29% accuracy).

Citation Download Citation

Bochao Ao and Bingbing Fan "An automatic annotation method based on federated learning", Proc. SPIE 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 126260I (28 April 2023); https://doi.org/10.1117/12.2674584

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