Abstract
Deep learning based image quality assessment (IQA) is useful for automatic quality control of medical images but requires a large number of training data. Though using multi-site data can significantly increase the training sample size and improve the performance of the IQA model, there are technical and legal issues involved in the sharing of patient data across different sites. When data are not sharable, devising a single IQA model that is applicable to all sites is challenging. To overcome this problem, we introduce a multi-site incremental IQA (MSI-IQA) method for structural MRI, which first trains an IQA model from one site, and then sequentially and incrementally improves the IQA model in other sites using transfer learning and consensus adversarial representation adaptation (CARA) without explicit data sharing between sites.
This work was supported in part by United States National Institutes of Health (NIH) grant EB006733 and the efforts of the UNC/UMN Baby Connectome Project Consortium.
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Liu, S., Thung, KH., Lin, W., Yap, PT. (2021). Multi-site Incremental Image Quality Assessment of Structural MRI via Consensus Adversarial Representation Adaptation. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12907. Springer, Cham. https://doi.org/10.1007/978-3-030-87234-2_36
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DOI: https://doi.org/10.1007/978-3-030-87234-2_36
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