Uncertainty-Aware Multi-Modal Ensembling for Severity Prediction of Alzheimer's Dementia

TL;DR

This paper introduces an uncertainty-aware multi-modal ensemble approach for predicting Alzheimer's Dementia severity, leveraging uncertainty estimates to improve robustness and outperform existing methods on a benchmark dataset.

Contribution

The work proposes a novel uncertainty-aware boosting technique for multi-modal ensembling in Alzheimer's severity prediction, enhancing robustness and accuracy.

Findings

Outperforms state-of-the-art methods on ADReSS dataset

Reduces system entropy indicating increased confidence

Effectively handles heteroscedastic data in multi-modal settings

Abstract

Reliability in Neural Networks (NNs) is crucial in safety-critical applications like healthcare, and uncertainty estimation is a widely researched method to highlight the confidence of NNs in deployment. In this work, we propose an uncertainty-aware boosting technique for multi-modal ensembling to predict Alzheimer's Dementia Severity. The propagation of uncertainty across acoustic, cognitive, and linguistic features produces an ensemble system robust to heteroscedasticity in the data. Weighing the different modalities based on the uncertainty estimates, we experiment on the benchmark ADReSS dataset, a subject-independent and balanced dataset, to show that our method outperforms the state-of-the-art methods while also reducing the overall entropy of the system. This work aims to encourage fair and aware models. The source code is available at https://github.com/wazeerzulfikar/alzheimers-dementia