论文标题
不确定性意识到的多模式结合对阿尔茨海默氏痴呆的严重性预测
Uncertainty-Aware Multi-Modal Ensembling for Severity Prediction of Alzheimer's Dementia
论文作者
论文摘要
神经网络（NNS）的可靠性在医疗保健等安全至关重要的应用中至关重要，不确定性估计是一种广泛研究的方法，可以突出NNS对部署的信心。在这项工作中，我们提出了一种不确定性感知的增强技术，用于多模式结合，以预测阿尔茨海默氏症的痴呆症严重程度。声学，认知和语言特征之间不确定性的传播产生了一个集合系统，可与数据中的异质性稳定。根据不确定性估计来权衡不同的模态，我们在基准Adress数据集（一种独立于主题且平衡的数据集）上进行实验，以表明我们的方法的表现优于最先进的方法，同时降低了系统的整体熵。这项工作旨在鼓励公平和意识的模型。源代码可从https://github.com/wazeerzulfikar/alzheimers-dementia获得
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
