Hua Zou

In preoperative multimodal cataract data, SLO images are often missing, and prior methods mostly suppressed the effect of the missing modality during fusion or filled it with zeros rather than explicitly recovering a task-relevant representation for it. This work treats missing SLO as proxy representation recovery in latent feature space: OCT image features and clinical text features condition a diffusion model whose reverse denoising estimates a sample-specific SLO proxy representation, which then enters the fusion prediction network. On 2,141 preoperative samples with a 58.80% SLO missing rate, the method reached 0.9714 accuracy under an absolute error within 0.1 LogMAR and a mean absolute error of 0.0423, versus 0.9625 and 0.0457 for the UML backbone baseline.

In preoperative multimodal cataract data, SLO images are often missing, and prior methods mostly suppressed the effect of the missing modality during fusion or filled it with zeros rather than explicitly recovering a task-relevant representation for it. This work treats missing SLO as proxy representation recovery in latent feature space: OCT image features and clinical text features condition a diffusion model whose reverse denoising estimates a sample-specific SLO proxy representation, which then enters the fusion prediction network. On 2,141 preoperative samples with a 58.80% SLO missing rate, the method reached 0.9714 accuracy under an absolute error within 0.1 LogMAR and a mean absolute error of 0.0423, versus 0.9625 and 0.0457 for the UML backbone baseline.