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.

翻译：多模态眼病筛查在眼科领域至关重要，因为它整合来自不同来源的信息以互补各自的性能。然而，现有方法在评估每个单模态的可靠性方面存在不足，直接融合不可靠模态可能导致筛查错误。为解决这一问题，我们提出了一种新颖的多模态证据融合眼病筛查流程——EyeMoSt，该流程能够衡量单模态的置信度，并从多分布融合的角度优雅地整合多模态信息。具体而言，我们的模型分别估计单模态的局部不确定性和融合模态的全局不确定性，以产生可靠的分类结果。更重要的是，所提出的学生t分布混合能够自适应地整合不同模态，赋予模型重尾特性，从而增强鲁棒性和可靠性。我们在公开数据集和内部数据集上的实验结果表明，我们的模型比现有方法更为可靠。此外，EyeMoSt具有作为数据质量判别器的潜在能力，能够为多模态眼病筛查提供可靠的决策支持。