Brain-Atlas-Guided Generative Counterfactual Attention for Explainable Cognitive Decline Diagnosis Using Multimodal Connectomes
arXiv:2606.01237v1 Announce Type: new Abstract: Mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are closely associated with the early Alzheimer's disease continuum, where accurate and explainable diagnosis is important for early risk assessment and intervention. Existing connectome-based deep learning models can improve classification performance but often provide limited insight into disease-related functional and structural connectivity changes. This paper proposes an atlas-knowledge-guided Generative Counterfactual Attention-guided Network (GCAN) for explainable cogni
The continuous advancements in AI and deep learning provide new tools to tackle complex medical challenges like early Alzheimer's diagnosis, building on recent progress in multimodal data analysis.
Accurate and explainable early diagnosis of cognitive decline is crucial for timely interventions, improving patient outcomes and reducing the societal burden of neurodegenerative diseases.
This research introduces a more transparent and interpretable AI model for cognitive decline diagnosis, moving beyond 'black box' classifications to provide insights into disease-related brain connectivity changes.
- · Patients with cognitive decline
- · Healthcare providers
- · AI in medicine developers
- · Neuroscience researchers
- · Traditional diagnostic methods
- · Healthcare systems unprepared for AI integration
Improved early detection rates for Alzheimer's and related cognitive impairments.
Accelerated development of targeted treatments and personalized intervention strategies based on detailed connectivity insights.
Potential for AI-driven preventative healthcare programs that monitor and predict neurological health years in advance.
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Read at arXiv cs.AI