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Revolutionizing Space Health (Swin-FSR): Advancing Super-Resolution of Fundus Images for SANS Visual Assessment Technology
However, due to the unavailability of experts in these locations, the data has to be transferred to an urban healthcare facility (AMD and glaucoma) or a terrestrial station (e. g, SANS) for more precise disease identification.
GitHub Link
The GitHub link is https://github.com/FarihaHossain/SwinFSRIntroduce
The repository "FarihaHossain/SwinFSR" on GitHub pertains to the project "Swin-FSR" presented at MICCAI 2023. The project focuses on enhancing the super-resolution of fundus images for SANS visual assessment technology. The code's prerequisites involve Ubuntu 18.04 or Windows 7, an NVIDIA graphics card, and various software installations including Nvidia drivers, Cuda Toolkit, CuDNN, Tensorflow-Gpu, and Keras. Additionally, the repository provides instructions for setting up the required Python environment and installing necessary packages via a requirements.txt file. However, due to the unavailability of experts in these locations, the data has to be transferred to an urban healthcare facility (AMD and glaucoma) or a terrestrial station (e. g, SANS) for more precise disease identification.Content
This code is for our paper "Revolutionizing Space Health (Swin-FSR): Advancing Super-Resolution of Fundus Images for SANS Visual Assessment Technology" which has been accepted in MICCAI 2023.
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