A Weighted Vision Transformer-Based Multi-Task Learning Framework for Predicting ADAS-Cog Scores

TL;DR

This paper introduces a weighted Vision Transformer-based multi-task learning framework that predicts ADAS-Cog scores, emphasizing the importance of sub-score weighting to improve accuracy and interpretability in Alzheimer's disease prognosis.

Contribution

It proposes a novel weighted ViT-based multi-task learning model that systematically explores sub-score-specific loss weighting for better AD score prediction from MRI data.

Findings

Weighted strategies improve prediction accuracy for heterogeneous MCI subjects.

Moderate weighting enhances performance for cognitively normal subjects.

Uniform weighting underperforms compared to tailored weighting approaches.

Abstract

Prognostic modeling is essential for forecasting future clinical scores and enabling early detection of Alzheimers disease (AD). While most existing methods focus on predicting the ADAS-Cog global score, they often overlook the predictive value of its 13 sub-scores, which reflect distinct cognitive domains. Some sub-scores may exert greater influence on determining global scores. Assigning higher loss weights to these clinically meaningful sub-scores can guide the model to focus on more relevant cognitive domains, enhancing both predictive accuracy and interpretability. In this study, we propose a weighted Vision Transformer (ViT)-based multi-task learning (MTL) framework to jointly predict the ADAS-Cog global score using baseline MRI scans and its 13 sub-scores at Month 24. Our framework integrates ViT as a feature extractor and systematically investigates the impact of sub-score-specific loss weighting on model performance. Results show that our proposed weighting strategies are group-dependent: strong weighting improves performance for MCI subjects with more heterogeneous MRI patterns, while moderate weighting is more effective for CN subjects with lower variability. Our findings suggest that uniform weighting underutilizes key sub-scores and limits generalization. The proposed framework offers a flexible, interpretable approach to AD prognosis using end-to-end MRI-based learning. (Github repo link will be provided after review)