Abstract
Most intelligent diagnosis systems are developed for one or a few specific diseases, while medical specialists can diagnose all diseases of certain organ or tissue. Since it is often difficult to collect data of all diseases, it would be desirable if an intelligent system can initially diagnose a few diseases, and then continually learn to diagnose more and more diseases with coming data of these new classes in the future. However, current intelligent systems are characterised by catastrophic forgetting of old knowledge when learning new classes. In this paper, we propose a new continual learning framework to alleviate this issue by simultaneously distilling both old knowledge and recently learned new knowledge and by ensembling the class-specific knowledge from the previous classifier and the learned new classifier. Experiments showed that the proposed method outperforms state-of-the-art methods on multiple medical and natural image datasets.
Z. Li and C. Zhong—The authors contribute equally to this paper.
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Acknowledgement
This work is supported in part by the National Key Research and Development Program (grant No. 2018YFC1315402), the Guangdong Key Research and Development Program (grant No. 2019B020228001), the National Natural Science Foundation of China (grant No. U1811461), and the Guangzhou Science and Technology Program (grant No. 201904010260).
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Li, Z., Zhong, C., Wang, R., Zheng, WS. (2020). Continual Learning of New Diseases with Dual Distillation and Ensemble Strategy. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12261. Springer, Cham. https://doi.org/10.1007/978-3-030-59710-8_17
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