Discriminative ensemble meta-learning with co-regularization for rare fundus diseases diagnosis
Deep neural networks (DNNs) have been widely applied in the medical image community, contributing to automatic ophthalmic screening systems for some common diseases. However, the incidence of fundus diseases patterns exhibits a typical long-tailed distribution. In clinic, a small number of common fundus diseases have sufficient observed cases for large-scale analysis while most of the fundus diseases are infrequent. For these rare diseases with extremely low-data regimes, it is challenging to train DNNs to realize automatic diagnosis. In this work, we develop an automatic diagnosis system for rare fundus diseases, based on the meta-learning framework. The system incorporates a co-regularization loss and the ensemble-learning strategy into the meta-learning framework, fully leveraging the advantage of multi-scale hierarchical feature embedding. We initially conduct comparative experiments on our newly-constructed lightweight multi-disease fundus images dataset for the few-shot recognition task (namely, FundusData-FS). Moreover, we verify the cross-domain transferability from miniImageNet to FundusData-FS, and further confirm our method's good repeatability. Rigorous experiments demonstrate that our method can detect rare fundus diseases, and is superior to the state-of-the-art methods. These investigations demonstrate that the potential of our method for the real clinical practice is promising.
Natural Science Foundation of Beijing Municipality[Z210008];
|WOS Research Area|
Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
|WOS Accession No|
|ESI Research Field|
Cited Times [WOS]:0
|Document Type||Journal Article|
|Department||Department of Computer Science and Engineering|
1.Institute of Medical Technology,Peking University Health Science Center,Peking University,Beijing,100191,China
2.Department of Biomedical Engineering,College of Future Technology,Peking University,Beijing,100871,China
3.National Biomedical Imaging Center,Peking University,Beijing,100871,China
4.Institute of Biomedical Engineering,Peking University Shenzhen Graduate School,Shenzhen,518055,China
5.Institute of Biomedical Engineering,Shenzhen Bay Laboratory 5F,Shenzhen,518071,China
6.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
Gao，Mengdi,Jiang，Hongyang,Zhu，Lei,et al. Discriminative ensemble meta-learning with co-regularization for rare fundus diseases diagnosis[J]. Medical Image Analysis,2023,89.
Gao，Mengdi.,Jiang，Hongyang.,Zhu，Lei.,Jiang，Zhe.,Geng，Mufeng.,...&Lu，Yanye.(2023).Discriminative ensemble meta-learning with co-regularization for rare fundus diseases diagnosis.Medical Image Analysis,89.
Gao，Mengdi,et al."Discriminative ensemble meta-learning with co-regularization for rare fundus diseases diagnosis".Medical Image Analysis 89(2023).
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