Chengcheng Huang, Yukang Jiang, Xiaochun Yang, Chiyu Wei, Hongyu Chen, Weixue Xiong, Henghui Lin, Xueqin Wang, Ting Tian, Haizhu Tan
PURPOSE: The assessment of retinal image (RI) quality holds significant importance in both clinical trials and large datasets, because suboptimal images can potentially conceal early signs of diseases, thereby resulting in inaccurate medical diagnoses. This study aims to develop an automatic method for Retinal Image Quality Assessment (RIQA) that incorporates visual explanations, aiming to comprehensively evaluate the quality of retinal fundus images (RIs). METHODS: We developed an automatic RIQA system, named Swin-MCSFNet, utilizing 28,792 RIs from the EyeQ dataset, as well as 2000 images from the EyePACS dataset and an additional 1,000 images from the OIA-ODIR dataset...
April 2, 2024: Translational Vision Science & Technology