Abstract
Retinitis pigmentosa (RP) is one of the most common retinal diseases caused by gene defects, which can lead to night blindness or complete blindness. Accurate diagnosis and lesion identification are significant tasks for clinicians to assess fundus images. However, due to some limitations, it is still challenging to design a method that can simultaneously diagnose and accomplish lesion identification so that the accurate lesion identification can promote the accuracy of diagnosis. In this paper, we propose a method based on weakly-supervised lesion-aware and consistency regularization to detect RP and generate lesion attention map (LAM). Specifically, we extend global average pooling to multiple scales, and use multi-scale features to offset the gap between semantic information and spatial information to generate a more refined LAM. At the same time, we regularize LAMs with different affine transforms for the same sample, and force them to produce more accurate predictions and reduce the overconfidence of the network, which can enhance LAM to cover lesions. We use two central datasets to verify the effectiveness of the proposed model. We train the proposed model in one dataset and test it in the other dataset to verify the generalization performance. Experimental results show that our method achieves promising performance.
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Acknowledgements
This work was supported partly by National Natural Science Foundation of China (Nos. 61871274, 61801305 and 81571758), National Natural Science Foundation of Guangdong Province (No. 2020A1515010649 and No. 2019A1515 111205), Guangdong Province Key Laboratory of Popular High Performance Computers (No. 2017B030314073), Guangdong Laboratory of Artificial-Intelligence and Cyber-Economics (SZ), Shenzhen Peacock Plan (Nos. KQTD2016053112051497 and KQTD2015033016104926), Shenzhen Key Basic Research Project (Nos. JCYJ201908 08165209410, 20190808145011259, JCYJ20180507184647636, GJHZ20190822095 414576 and JCYJ20170302153337765, JCYJ20170302150411789, JCYJ2017030214 2515949, GCZX2017040715180580, GJHZ20180418190529516, and JSGG2018050 7183215520), NTUT-SZU Joint Research Program (No. 2020003), Special Project in Key Areas of Ordinary Universities of Guangdong Province (No. 2019KZDZX1015).
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Zhao, B. et al. (2020). Weakly-Supervised Lesion-Aware and Consistency Regularization for Retinitis Pigmentosa Detection from Ultra-Widefield Images. In: Fu, H., Garvin, M.K., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2020. Lecture Notes in Computer Science(), vol 12069. Springer, Cham. https://doi.org/10.1007/978-3-030-63419-3_18
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DOI: https://doi.org/10.1007/978-3-030-63419-3_18
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