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

2025-08-25 · Nur Amirah Abd Hamid, Mohd Ibrahim Shapiai, Daphne Teck Ching Lai arxiv

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)

📄 PDF Abstract BibTeX arXiv:2508.17613

Code (0)

등록된 구현이 없습니다.

Tasks

Multi-Task Learning

MOOZY: A Patient-First Foundation Model for Computational Pathology

2026-03-27 · Yousef Kotp, Vincent Quoc-Huy Trinh, Christopher Pal, Mahdi S. Hosseini arxiv

Computational pathology needs whole-slide image (WSI) foundation models that transfer across diverse clinical tasks, yet current approaches remain largely slide-centric, often depend on private data and expensive paired-…

ThreatVisionAI: A Hybrid CNN-ViT Framework for Image-Based Malware Classification

2026-07-04 · Allyson Taylor, Prashanth BusiReddyGari arxiv

Traditional malware detection methods struggle to generalize to obfuscated or previously unseen threats. This paper introduces ThreatVisionAI, a hybrid malware family classification framework that integrates a raw-image …

Malware Classification Malware Detection

Retriv at BLP-2025 Task 1: A Transformer Ensemble and Multi-Task Learning Approach for Bangla Hate Speech Identification

2025-11-10 · Sourav Saha, K M Nafi Asib, Mohammed Moshiul Hoque arxiv

This paper addresses the problem of Bangla hate speech identification, a socially impactful yet linguistically challenging task. As part of the "Bangla Multi-task Hate Speech Identification" shared task at the BLP Worksh…

Hate Speech Detection Multi-Task Learning

Bridging Graph Position Encodings for Transformers with Weighted Graph-Walking Automata

2022-12-13 · Patrick Soga, David Chiang

A current goal in the graph neural network literature is to enable transformers to operate on graph-structured data, given their success on language and vision tasks. Since the transformer's original sinusoidal positiona…

Graph Neural Network Machine Translation Position

R-Cut: Enhancing Explainability in Vision Transformers with Relationship Weighted Out and Cut

2023-07-18 · Yingjie Niu, Ming Ding, Maoning Ge, Robin Karlsson 외

Transformer-based models have gained popularity in the field of natural language processing (NLP) and are extensively utilized in computer vision tasks and multi-modal models such as GPT4. This paper presents a novel met…

image-classification Image Classification