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Despite the promise of multimodal fusion, existing architectures struggle to surpass single-tracer performance in PET/CT segmentation, particularly for FDG.
Generated retinal images can inherit critical clinical information, but they may not perform well against real-world classifiers, exposing a crucial representation gap.
