In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. Lugmayr, A., Danelljan, M., Timofte, R.: Unsupervised learning for real-world super-resolution. Thakoor, K.A., et al.: A multimodal deep learning system to distinguish late stages of AMD and to compare expert vs. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. Thakoor, K., Bordbar, D., Yao, J., Moussa, O., Chen, R., Sajda, P.: Hybrid 3D–2D deep learning for detection of neovascularage-related macular degeneration using optical coherence tomography B-scans and angiography volumes. 1125–1134 (2017)Īrmanious, K., et al.: MedGAN: medical image translation using GANs. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. Wang, X., et al.: ESRGAN: enhanced super-resolution generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. Krull, A., Buchholz, T.O., Jug, F.: Noise2Void-learning denoising from single noisy images. Lehtinen, J., et al.: Noise2Noise: learning image restoration without clean data. Zheng, C., et al.: Assessment of generative adversarial networks model for synthetic optical coherence tomography images of retinal disorders. Tavakkoli, A., Kamran, S.A., Hossain, K.F., Zuckerbrod, S.L.: A novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs. Song, G., et al.: First clinical application of low-cost OCT. Kim, S., Crose, M., Eldridge, W.J., Cox, B., Brown, W.J., Wax, A.: Design and implementation of a low-cost, portable OCT system. Teikari, P., Najjar, R.P., Schmetterer, L., Milea, D.: Embedded deep learning in ophthalmology: making ophthalmic imaging smarter. Moraru, A.D., Costin, D., Moraru, R.L., Branisteanu, D.C.: Artificial intelligence and deep learning in ophthalmology-present and future. Keywordsīriganti, G., Le Moine, O.: Artificial intelligence in medicine: today and tomorrow. By exhibiting proof-of-principle AI-based AMD detection even on low-quality p-OCT data, this study stimulates future work toward low-cost, portable imaging+AI systems for eye disease detection. We also achieve denoising after image-to-image translation. Using GANs trained on simulated p-OCT data generated from paired commercial OCT data degraded with the point spread function (PSF) of the p-OCT device, we observe improved AI performance on p-OCT data after single-image super-resolution. We use generative adversarial networks (GANs) to enhance the quality of this p-OCT data and then assess the impact of this enhancement on downstream performance of artificial intelligence (AI) algorithms for AMD detection. In this study, we focus on data acquired with a low-cost, portable OCT (p-OCT) device, characterized by lower resolution, scanning rate, and imaging depth than a commercial OCT system. alone, only 15.3% of diabetic patients meet the recommendation of obtaining annual eye exams the situation is even worse for minority/under-served populations. Such constraints make it difficult for OCT to be accessible in low-resource settings. However, OCT systems are often bulky and expensive, costing tens of thousands of dollars and weighing on the order of 50 pounds or more. Using a low-coherence-length light source, OCT is able to achieve high axial resolution in biological samples this depth information is used by ophthalmologists to assess retinal structures and characterize disease states. Optical coherence tomography (OCT) is widely used for detection of ophthalmic diseases, such as glaucoma, age-related macular degeneration (AMD), and diabetic retinopathy.
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