Confidence-aware multi-modality learning for eye disease screening

TL;DR

This paper introduces a confidence-aware multi-modality fusion method for eye disease screening that models uncertainty and enhances robustness, reliability, and generalization in multi-modal ophthalmic image classification.

Contribution

It proposes a novel evidential fusion framework using Student's t distributions to incorporate confidence measures and improve robustness against noise and missing modalities.

Findings

Outperforms existing methods in robustness under noise and missing data.

Demonstrates strong generalization to out-of-distribution data.

Enhances reliability and accuracy through confidence ranking regularization.

Abstract

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.