Abstract
In various clinical scenarios, medical image is crucial in disease diagnosis and treatment. Different modalities of medical images provide complementary information and jointly helps doctors to make accurate clinical decision. However, due to clinical and practical restrictions, certain imaging modalities may be unavailable nor complete. To impute missing data with adequate clinical accuracy, here we propose a framework called self-supervised collaborative learning to synthesize missing modality for medical images. The proposed method comprehensively utilize all available information correlated to the target modality from multi-source-modality images to generate any missing modality in a single model. Different from the existing methods, we introduce an auto-encoder network as a novel, self-supervised constraint, which provides target-modality-specific information to guide generator training. In addition, we design a modality mask vector as the target modality label. With experiments on multiple medical image databases, we demonstrate a great generalization ability as well as specialty of our method compared with other state-of-the-arts.
Original language | English |
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Title of host publication | AAAI 2020 - 34th AAAI Conference on Artificial Intelligence |
Publisher | AAAI press |
Pages | 10486-10493 |
Number of pages | 8 |
ISBN (Electronic) | 9781577358350 |
Publication status | Published - 2020 |
Event | 34th AAAI Conference on Artificial Intelligence, AAAI 2020 - New York, United States Duration: 2020 Feb 7 → 2020 Feb 12 |
Publication series
Name | AAAI 2020 - 34th AAAI Conference on Artificial Intelligence |
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Conference
Conference | 34th AAAI Conference on Artificial Intelligence, AAAI 2020 |
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Country/Territory | United States |
City | New York |
Period | 20/2/7 → 20/2/12 |
Bibliographical note
Funding Information:B.C., N.W., and X.G. were supported in part by the National Natural Science Foundation of China (61922066, 61876142, 61671339, 61772402, U1605252, and 61432014), the National Key Research and Development Program of China (2016QY01W0200 and 2018AAA0103202), the National High-Level Talents Special Support Program of China (CS31117200001), the Fundamental Research Funds for the Central Universities (JB190117), the Innovation Fund of Xidian University, and the Xidian University-Intellifusion Joint Innovation Laboratory of Artificial Intelligence.
Publisher Copyright:
Copyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
ASJC Scopus subject areas
- Artificial Intelligence