论文标题
从预训练的网络中利用多模式特征来识别阿尔茨海默氏症的痴呆症识别
Exploiting Multi-Modal Features From Pre-trained Networks for Alzheimer's Dementia Recognition
论文作者
论文摘要
收集和访问大量医疗数据非常耗时且费力，这不仅是因为很难找到特定的患者，而且还因为解决患者病历的机密性所必需。另一方面，有深度学习模型，对易于收藏的大规模数据集进行了培训，例如YouTube或Wikipedia，提供了有用的表示形式。因此，利用这些预训练网络的功能来处理手头少量数据可能非常有利。在这项工作中，我们利用了从预训练的网络中提取的各种多模式特征，使用神经网络识别阿尔茨海默氏症的痴呆症，并在Interspeech 2020年的Adress Challenge提供了一个小数据集。挑战是可疑患者通过提供声音和文本和文本数据来识别对阿尔茨海默氏症的痴呆症的挑战。借助多模式特征，我们修改了基于卷积的神经网络结构，以同时执行分类和回归任务，并能够计算具有可变长度的对话。我们的测试结果超过了基线的准确性18.75％，我们对回归任务的验证结果表明，将4类认知障碍分类的可能性为78.70％。
Collecting and accessing a large amount of medical data is very time-consuming and laborious, not only because it is difficult to find specific patients but also because it is required to resolve the confidentiality of a patient's medical records. On the other hand, there are deep learning models, trained on easily collectible, large scale datasets such as Youtube or Wikipedia, offering useful representations. It could therefore be very advantageous to utilize the features from these pre-trained networks for handling a small amount of data at hand. In this work, we exploit various multi-modal features extracted from pre-trained networks to recognize Alzheimer's Dementia using a neural network, with a small dataset provided by the ADReSS Challenge at INTERSPEECH 2020. The challenge regards to discern patients suspicious of Alzheimer's Dementia by providing acoustic and textual data. With the multi-modal features, we modify a Convolutional Recurrent Neural Network based structure to perform classification and regression tasks simultaneously and is capable of computing conversations with variable lengths. Our test results surpass baseline's accuracy by 18.75%, and our validation result for the regression task shows the possibility of classifying 4 classes of cognitive impairment with an accuracy of 78.70%.
