MC-ViViT: Multi-branch Classifier-ViViT to detect Mild Cognitive Impairment in older adults using facial videos

• URL: http://arxiv.org/abs/2304.05292v4
• Date: Fri, 5 Jan 2024 18:59:41 GMT
• Title: MC-ViViT: Multi-branch Classifier-ViViT to detect Mild Cognitive Impairment in older adults using facial videos
• Authors: Jian Sun, Hiroko H. Dodge, and Mohammad H. Mahoor
• Abstract summary: This paper proposes a novel Multi-branch-Video Vision Transformer (MCViViT) model to distinguish from those with normal cognition by analyzing facial features. The data comes from the I-CONECT, a behavioral intervention trial aimed at improving cognitive function by providing frequent video chats. Our experimental results on I-CONECT dataset show the great potential of MC-ViViT in predicting MCI with a high accuracy of 90.63%.
• Score: 44.72781467904852
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep machine learning models including Convolutional Neural Networks (CNN) have been successful in the detection of Mild Cognitive Impairment (MCI) using medical images, questionnaires, and videos. This paper proposes a novel Multi-branch Classifier-Video Vision Transformer (MC-ViViT) model to distinguish MCI from those with normal cognition by analyzing facial features. The data comes from the I-CONECT, a behavioral intervention trial aimed at improving cognitive function by providing frequent video chats. MC-ViViT extracts spatiotemporal features of videos in one branch and augments representations by the MC module. The I-CONECT dataset is challenging as the dataset is imbalanced containing Hard-Easy and Positive-Negative samples, which impedes the performance of MC-ViViT. We propose a loss function for Hard-Easy and Positive-Negative Samples (HP Loss) by combining Focal loss and AD-CORRE loss to address the imbalanced problem. Our experimental results on the I-CONECT dataset show the great potential of MC-ViViT in predicting MCI with a high accuracy of 90.63% accuracy on some of the interview videos.

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