Joint Segmentation and Grading with Iterative Optimization for Multimodal Glaucoma Diagnosis

ArXi:2603.14188v1 Announce Type: new Accurate diagnosis of glaucoma is challenging, as early-stage changes are subtle and often lack clear structural or appearance cues. Most existing approaches rely on a single modality, such as fundus or optical coherence tomography (OCT), capturing only partial pathological information and often missing early disease progression. In this paper, we propose an iterative multimodal optimization model (IMO) for joint segmentation and grading.