TY - GEN
T1 - A Coherent Cooperative Learning Framework Based on Transfer Learning for Unsupervised Cross-Domain Classification
AU - Shan, Xinxin
AU - Wen, Ying
AU - Li, Qingli
AU - Lu, Yue
AU - Cai, Haibin
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - In the practical application of medical image analysis, due to the different data distributions of source domain and target domain and the lack of the labels of target domain, domain adaptation for unsupervised cross-domain classification attracts widespread attention. However, current methods take knowledge transfer model and classification model as two separate training stages, which inadequately considers and utilizes the intrinsic information interaction between modules. In this paper, we propose a coherent cooperative learning framework based on transfer learning for unsupervised cross-domain classification. The proposed framework is constructed by two classifiers trained by transfer learning, which can respectively classify images of source domain and target domain, and a Wasserstein CycleGAN for image translation and data augmentation. In the coherent process, all modules are updated in turn, and the data is transferred between different modules to realize the knowledge transfer and collaborative training. The final prediction is obtained by a voting result of two classifiers. Experimental results on three pneumonia databases demonstrate the effectiveness of our framework with diverse backbones.
AB - In the practical application of medical image analysis, due to the different data distributions of source domain and target domain and the lack of the labels of target domain, domain adaptation for unsupervised cross-domain classification attracts widespread attention. However, current methods take knowledge transfer model and classification model as two separate training stages, which inadequately considers and utilizes the intrinsic information interaction between modules. In this paper, we propose a coherent cooperative learning framework based on transfer learning for unsupervised cross-domain classification. The proposed framework is constructed by two classifiers trained by transfer learning, which can respectively classify images of source domain and target domain, and a Wasserstein CycleGAN for image translation and data augmentation. In the coherent process, all modules are updated in turn, and the data is transferred between different modules to realize the knowledge transfer and collaborative training. The final prediction is obtained by a voting result of two classifiers. Experimental results on three pneumonia databases demonstrate the effectiveness of our framework with diverse backbones.
KW - Collaborative training
KW - Transfer learning
KW - Unsupervised cross-domain classification
KW - Wasserstein CycleGAN
UR - https://www.scopus.com/pages/publications/85116482404
U2 - 10.1007/978-3-030-87240-3_10
DO - 10.1007/978-3-030-87240-3_10
M3 - 会议稿件
AN - SCOPUS:85116482404
SN - 9783030872397
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 96
EP - 106
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
A2 - de Bruijne, Marleen
A2 - Cattin, Philippe C.
A2 - Cotin, Stéphane
A2 - Padoy, Nicolas
A2 - Speidel, Stefanie
A2 - Zheng, Yefeng
A2 - Essert, Caroline
PB - Springer Science and Business Media Deutschland GmbH
T2 - 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
Y2 - 27 September 2021 through 1 October 2021
ER -