TY - JOUR
T1 - Federated Active Learning for Multicenter Collaborative Disease Diagnosis
AU - Wu, Xing
AU - Pei, Jie
AU - Chen, Cheng
AU - Zhu, Yimin
AU - Wang, Jianjia
AU - Qian, Quan
AU - Zhang, Jian
AU - Sun, Qun
AU - Guo, Yike
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 62172267; in part by the National Key Research and Development Program of China under Grant 2019YFE0190500; in part by the Natural Science Foundation of Shanghai, China, under Grant 20ZR1420400; in part by the State Key Program of National Natural Science Foundation of China under Grant 61936001; and in part by the Key Research Project of Zhejiang Laboratory under Grant 2021PE0AC02.
Publisher Copyright:
© 2022 IEEE.
PY - 2023/7
Y1 - 2023/7
N2 - Current computer-aided diagnosis system with deep learning method plays an important role in the field of medical imaging. The collaborative diagnosis of diseases by multiple medical institutions has become a popular trend. However, large scale annotations put heavy burdens on medical experts. Furthermore, the centralized learning system has defects in privacy protection and model generalization. To meet these challenges, we propose two federated active learning methods for multicenter collaborative diagnosis of diseases: the Labeling Efficient Federated Active Learning (LEFAL) and the Training Efficient Federated Active Learning (TEFAL). The proposed LEFAL applies a task-agnostic hybrid sampling strategy considering data uncertainty and diversity simultaneously to improve data efficiency. The proposed TEFAL evaluates the client informativeness with a discriminator to improve client efficiency. On the Hyper-Kvasir dataset for gastrointestinal disease diagnosis, with only 65% of labeled data, the LEFAL achieves 95% performance on the segmentation task with whole labeled data. Moreover, on the CC-CCII dataset for COVID-19 diagnosis, with only 50 iterations, the accuracy and F1-score of TEFAL are 0.90 and 0.95, respectively on the classification task. Extensive experimental results demonstrate that the proposed federated active learning methods outperform state-of-the-art methods on segmentation and classification tasks for multicenter collaborative disease diagnosis.
AB - Current computer-aided diagnosis system with deep learning method plays an important role in the field of medical imaging. The collaborative diagnosis of diseases by multiple medical institutions has become a popular trend. However, large scale annotations put heavy burdens on medical experts. Furthermore, the centralized learning system has defects in privacy protection and model generalization. To meet these challenges, we propose two federated active learning methods for multicenter collaborative diagnosis of diseases: the Labeling Efficient Federated Active Learning (LEFAL) and the Training Efficient Federated Active Learning (TEFAL). The proposed LEFAL applies a task-agnostic hybrid sampling strategy considering data uncertainty and diversity simultaneously to improve data efficiency. The proposed TEFAL evaluates the client informativeness with a discriminator to improve client efficiency. On the Hyper-Kvasir dataset for gastrointestinal disease diagnosis, with only 65% of labeled data, the LEFAL achieves 95% performance on the segmentation task with whole labeled data. Moreover, on the CC-CCII dataset for COVID-19 diagnosis, with only 50 iterations, the accuracy and F1-score of TEFAL are 0.90 and 0.95, respectively on the classification task. Extensive experimental results demonstrate that the proposed federated active learning methods outperform state-of-the-art methods on segmentation and classification tasks for multicenter collaborative disease diagnosis.
KW - active learning
KW - Federated learning
KW - labeling-efficient
KW - multicenter
KW - training-efficient
UR - http://www.scopus.com/inward/record.url?scp=85144812567&partnerID=8YFLogxK
U2 - 10.1109/TMI.2022.3227563
DO - 10.1109/TMI.2022.3227563
M3 - Journal article
C2 - 37015520
AN - SCOPUS:85144812567
SN - 0278-0062
VL - 42
SP - 2068
EP - 2080
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 7
ER -