Abstract
Accurate differentiation between cognitively unimpaired individuals, mild cognitive impairment (MCI), and dementia remains challenging, particularly when heterogeneous data must be integrated within a single predictive framework. This study proposes a strategy-aware multimodal late-fusion approach for three-class classification using 2D MRI, 3D MRI, and tabular clinical data, explicitly accounting for different diagnostic priorities at the fusion stage. The best results were obtained by a balanced late-fusion configuration based on ResNet-18 for both imaging branches and Random Forest for tabular classification and fusion, achieving 0.96 accuracy, 0.97 macro-precision, 0.96 macro-recall, and 0.96 macro-F1, with strong recognition of the clinically challenging MCI class.
Paper Type
Poster
DOI
10.62036/ISD.2026.101
Strategy-Aware Multimodal Late Fusion for Three-Class Cognitive State Classification
Accurate differentiation between cognitively unimpaired individuals, mild cognitive impairment (MCI), and dementia remains challenging, particularly when heterogeneous data must be integrated within a single predictive framework. This study proposes a strategy-aware multimodal late-fusion approach for three-class classification using 2D MRI, 3D MRI, and tabular clinical data, explicitly accounting for different diagnostic priorities at the fusion stage. The best results were obtained by a balanced late-fusion configuration based on ResNet-18 for both imaging branches and Random Forest for tabular classification and fusion, achieving 0.96 accuracy, 0.97 macro-precision, 0.96 macro-recall, and 0.96 macro-F1, with strong recognition of the clinically challenging MCI class.
Recommended Citation
Wosiak, A., Truszkowska, M. & Żykwińska, K.(2026). Strategy-Aware Multimodal Late Fusion for Three-Class Cognitive State Classification. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.101