  Multimodality eye disease screening is crucial in ophthalmology as it
integrates information from diverse sources to complement their respective
performances. However, the existing methods are weak in assessing the
reliability of each unimodality, and directly fusing an unreliable modality may
cause screening errors. To address this issue, we introduce a novel
multimodality evidential fusion pipeline for eye disease screening, EyeMoSt,
which provides a measure of confidence for unimodality and elegantly integrates
the multimodality information from a multi-distribution fusion perspective.
Specifically, our model estimates both local uncertainty for unimodality and
global uncertainty for the fusion modality to produce reliable classification
results. More importantly, the proposed mixture of Student's $t$ distributions
adaptively integrates different modalities to endow the model with heavy-tailed
properties, increasing robustness and reliability. Our experimental findings on
both public and in-house datasets show that our model is more reliable than
current methods. Additionally, EyeMost has the potential ability to serve as a
data quality discriminator, enabling reliable decision-making for multimodality
eye disease screening.
