Abstract
More and more people are suffering from ocular diseases, which may cause blindness if not treated promptly. However, it is not easy to diagnose these diseases for the barely visible clinical symptoms. Even though some computer-aided approaches have been developed to help ophthalmologists make an accurate diagnosis, there still exist some challenges to be solved. For example, one patient may suffer from more than one retinal disease and these diseases often exhibit a long-tailed distribution, making it difficult to be automatically classified. In this work, we propose a novel framework that utilizes the correlations among these diseases in a knowledge distillation manner. Specifically, we apply the correlations from three main aspects (i.e., multi-task relation, feature relation, and pathological region relation) to recognize diseases more exactly. Firstly, we take diabetic retinopathy (DR) lesion segmentation and severity grading as the downstream tasks to train the network backbone for the findings that segmentation may improve the classification. Secondly, the long-tailed dataset is divided into several subsets to train multiple teacher networks according to semantic feature relation, which can help reduce the label co-occurrence and class imbalance. Thirdly, an improved attention mechanism is adopted to explore relations among pathological regions. Finally, a class-balanced distillation loss is introduced to distill the multiple teacher models into a student model. Extensive experiments are conducted to validate the superiority of our proposed method. The results have demonstrated that we achieve state-of-the-art performance on the publicly available datasets. Code will be available at: https://github.com/liyiersan/RLKD.
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Acknowledgements
This work is supported by Science and Technology Cooperation Project of The Xinjiang Production and Construction Corps (No. 2019BC008, 2017DB004).
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Zhou, Q., Zou, H., Wang, Z. (2022). Long-Tailed Multi-label Retinal Diseases Recognition via Relational Learning and Knowledge Distillation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13432. Springer, Cham. https://doi.org/10.1007/978-3-031-16434-7_68
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