To establish a multimodal fundus image report generation model to realize an interpretable system for multiple fundus diseases, multimodal image analysis, diagnosis, and treatment decision automatic reporting based on weakly labeled training data. Construct an interpretable feature fusion network for the clinical and imaging features of fundus lesions, and we hope to extract new imaging markers that can predict the occurrence and progression of various fundus lesions at an early stage, and ultimately verify them in real clinical data, further providing possible directions for exploring the molecular mechanisms of refractory fundus lesions, and may also provide new ideas for the precise prevention and treatment of fundus lesions.
Disease group: All multimodal fundus examination images containing fundus lesions, examined from January 2011 to December 2023, including fundus photography, as well as OCT, OCTA, FFA, ICGA, B-ultrasound and corresponding imaging reports. Images could be either clear or unclear, reports are either complete or incomplete.
Normal group: All multimodal fundus examination images without fundus lesions, examined from January 2011 to December 2023, including fundus photography, as well as OCT, OCTA, FFA, ICGA, B-ultrasound and corresponding imaging reports. Images could be either clear or unclear, reports are either complete or incomplete.
Disease group: 1. The image has serious quality problems; 2. The diagnostic report lacks key information.
Normal group: 1. The image has serious quality problems; 2. The diagnostic report lacks key information.