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Poster
MetaTeacher: Coordinating Multi-Model Domain Adaptation for Medical Image Classification
Zhenbin Wang · Mao Ye · Xiatian Zhu · Liuhan Peng · Liang Tian · Yingying Zhu

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In medical image analysis, we often need to build an image recognition system for a target scenario with the access to small labeled data and abundant unlabeled data, as well as multiple related models pretrained on different source scenarios. This presents the combined challenges of multi-source-free domain adaptation and semi-supervised learning simultaneously. However, both problems are typically studied independently in the literature, and how to effectively combine existing methods is non-trivial in design. In this work, we introduce a novel MetaTeacher framework with three key components: (1) A learnable coordinating scheme for adaptive domain adaptation of individual source models, (2) A mutual feedback mechanism between the target model and source models for more coherent learning, and (3) A semi-supervised bilevel optimization algorithm for consistently organizing the adaption of source models and the learning of target model. It aims to leverage the knowledge of source models adaptively whilst maximize their complementary benefits collectively to counter the challenge of limited supervision. Extensive experiments on five chest x-ray image datasets show that our method outperforms clearly all the state-of-the-art alternatives. The code is available at https://github.com/wongzbb/metateacher.

Author Information

Zhenbin Wang (University of Electronic Science and Technology of China)
Mao Ye (School of Computer Science and Engineering, University of Electronic Science and Technology of China)

Professor of School of Computer Science and Engineering, University of Electronic Science and Technology of China. He is the member of computer vision expert committee and multimedia computing expert committee of computer society. In 2002, he received his Ph.D. in Computational Mathematics from the Chinese University of Hong Kong and joined the University of Electronic Science and Technology of China. He has been selected into the new century excellent talents support plan of the Ministry of education and the Sichuan outstanding youth discipline leader support plan. At present, the main research fields are machine learning and computer vision, and more than 100 international academic papers have been published. He presided over various national, provincial and ministerial level projects such as the national key research and development program, the National Natural Science Foundation, and the science and Technology Department of Sichuan Province. He has served as the editorial board member of Engineering Applications of Artificial Intelligence, and the editorial board member of ZTE technology. He won one first prize of scientific and technological progress award of Sichuan Province and one second prize of science and technology award of China image graphics society. He also won the excellent cooperation team of Huawei University of Electronic Science and technology in 2012 and the best student paper award of 2017 ICME International Conference as a supervisor.

Xiatian Zhu (University of Surrey)
Liuhan Peng (Xinjiang University)
Liang Tian (University of Electronic Science and Technology of China)
Yingying Zhu (University of Texas, Arlington)

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