Decomposing Cross-Modality Failure in Diabetic Retinopathy Grading

Colour and near-infrared (NIR) fundus imaging depict the same retina through different image representations. A diabetic-retinopathy (DR) grader trained on colour photographs must therefore preserve disease knowledge while accommodating a new channel structure and intensity distribution. Across eight encoder configurations and five paired seeds, zero-shot colour-to-NIR transfer degrades sharply without a corresponding loss of confidence. We factorise the source intervention into channel representation and marginal intensity alignment: each recovers part of the loss, while their combination raises ResNet-50 external QWK from 0.08 to 0.54 and improves every tested backbone. A frozen-model control and layer-wise probes show that much of the failure can be reduced without learning a cross-domain mapping. Predictive uncertainty remains weak for shift detection, whereas Mahalanobis distance reaches OOD AUROC 0.93 and remains informative for residual grading errors. The resulting decomposition separates an input-addressable component from what remains for adaptation, acquisition, or new data.