  Multi-modal ophthalmic image classification plays a key role in diagnosing
eye diseases, as it integrates information from different sources to complement
their respective performances. However, recent improvements have mainly focused
on accuracy, often neglecting the importance of confidence and robustness in
predictions for diverse modalities. In this study, we propose a novel
multi-modality evidential fusion pipeline for eye disease screening. It
provides a measure of confidence for each modality and elegantly integrates the
multi-modality information using a multi-distribution fusion perspective.
Specifically, our method first utilizes normal inverse gamma prior
distributions over pre-trained models to learn both aleatoric and epistemic
uncertainty for uni-modality. Then, the normal inverse gamma distribution is
analyzed as the Student's t distribution. Furthermore, within a
confidence-aware fusion framework, we propose a mixture of Student's t
distributions to effectively integrate different modalities, imparting the
model with heavy-tailed properties and enhancing its robustness and
reliability. More importantly, the confidence-aware multi-modality ranking
regularization term induces the model to more reasonably rank the noisy
single-modal and fused-modal confidence, leading to improved reliability and
accuracy. Experimental results on both public and internal datasets demonstrate
that our model excels in robustness, particularly in challenging scenarios
involving Gaussian noise and modality missing conditions. Moreover, our model
exhibits strong generalization capabilities to out-of-distribution data,
underscoring its potential as a promising solution for multimodal eye disease
screening.
