Artifact Reduction in Fundus Imaging using Cycle Consistent Adversarial Neural Networks
This addresses misdiagnosis risks in ophthalmology by enhancing image quality, but it is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of artifact reduction in fundus images to improve visibility for diagnosing ophthalmic disorders, using a CycleGAN-based model with residual blocks, and reported significant improvements over existing techniques.
Fundus images are very useful in identifying various ophthalmic disorders. However, due to the presence of artifacts, the visibility of the retina is severely affected. This may result in misdiagnosis of the disorder which may lead to more complicated problems. Since deep learning is a powerful tool to extract patterns from data without much human intervention, they can be applied to image-to-image translation problems. An attempt has been made in this paper to automatically rectify such artifacts present in the images of the fundus. We use a CycleGAN based model which consists of residual blocks to reduce the artifacts in the images. Significant improvements are seen when compared to the existing techniques.