Abstract
Retinal-related diseases are the leading cause of vision loss and even blindness. The automatic methods for retinal disease segmentation based on medical images are essential for timely treatment. Fully supervised deep learning models have been successfully applied to medical image segmentation, which, however, particularly relies on a large number of pixel-level labels. To reduce the model’s dependence on pixel-level labels, in this paper, we propose a progressive refined generative adversarial network (PRGAN) model by translating the abnormal retinal images to healthy images. The diseased area can be revealed by differentiating the synthetic healthy and real diseased images, thereby realizing the segmentation of lesions. The proposed framework is a weakly supervised approach with image-level annotations. A progressive generator is proposed with multi-scale strategy to generate images with more detail for large-scale color fundus images by refining the synthesis scale by scale. A visual consistency module is introduced to preserve color information in source domain images. Moreover, the domain classification loss is utilized to improve the convergence of the model. Our model achieves superior classification and segmentation performance on the IDRiD, DDR and Ichallenge-AMD datasets. With clear localization for lesion areas, the competitive results reveal great potentials for the proposed weakly supervised model.
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This work was supported by the National Natural Science Foundation of China under Grant NO. 62072241.
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Chen, A., Ma, X., Chen, Q., Ji, Z. (2022). PRGAN: A Progressive Refined GAN for Lesion Localization and Segmentation on High-Resolution Retinal Fundus Photography. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13535. Springer, Cham. https://doi.org/10.1007/978-3-031-18910-4_23
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