MLCDT: fine-grained multi-task learning for enhanced cognitive assessment in the clock drawing test

Lei Yao; Haomiao Ma; Haoran Sun; Yingwei Zhang; Shili Liang; Lei Zhang; Pan Shang; Shiwen Sun

doi:10.1117/12.3055764

9 January 2025 MLCDT: fine-grained multi-task learning for enhanced cognitive assessment in the clock drawing test

Lei Yao, Haomiao Ma, Haoran Sun, Yingwei Zhang, Shili Liang, Lei Zhang, Pan Shang, Shiwen Sun

Author Affiliations +

Proceedings Volume 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024); 134860K (2025) https://doi.org/10.1117/12.3055764
Event: Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 2024, Chengdu, China

Abstract

Artificial Intelligence (AI) is increasingly used in cognitive health assessments, with the Clock Drawing Test (CDT) being an effective cognitive evaluation tool. However, the complexity of CDT image structures, high subjectivity, and the lack of specialized cognitive health assessment datasets for specific populations pose significant challenges for feature learning and model construction using this method. To address these issues, we propose a fine-grained multi-task learning approach (MLCDT) for AI-assisted diagnosis of cognitive health using CDT. MLCDT integrates image pre-training models with a multi-task learning framework to capture fine-grained features of CDT images and constructs a final diagnostic support model through scientifically designed tasks. Experiments using real data from cognitive health assessments in a neurology department at a hospital validate the effectiveness of MLCDT in handling fine-grained tasks and aiding cognitive disorder assessments.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lei Yao, Haomiao Ma, Haoran Sun, Yingwei Zhang, Shili Liang, Lei Zhang, Pan Shang, and Shiwen Sun "MLCDT: fine-grained multi-task learning for enhanced cognitive assessment in the clock drawing test", Proc. SPIE 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 134860K (9 January 2025); https://doi.org/10.1117/12.3055764

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KEYWORDS

Machine learning

Data modeling

Education and training

Alzheimer disease

Cognitive modeling

Transformers

Clocks

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