arXiv:2607.01901v1 Announce Type: new Abstract: Effective brain disease diagnosis requires the synergy of brain connectivity patterns and high-level semantic knowledge. Existing methods, however, largely treat semantics from large language models (LLMs) as auxiliary features or supervision, limiting their direct role in decision-making and constraining classification stability and robustness. To overcome this, we propose a semantic-aligned brain network framework that actively integrates LLM-derived semantics into the prediction process. Specifically, ROI-level semantics are first incorporated
The proliferation of advanced LLMs and increasing computational power allows for the integration of high-level semantic knowledge into complex analytical frameworks like brain network analysis.
This development proposes a more stable and robust method for disease diagnosis and understanding brain function, moving beyond treating LLM outputs as mere auxiliary features.
Traditional brain network analysis methods are augmented by direct semantic integration from LLMs, potentially leading to more accurate and reliable diagnostic tools.
- · AI healthcare startups
- · Medical diagnostics sector
- · Neurology research
- · LLM developers
- · Traditional brain network analysis methods without semantic integration
Improved diagnosis and treatment strategies for neurological disorders.
Accelerated discovery of new brain-related biomarkers and therapeutic targets.
Potential for personalized brain health models and preventative interventions based on deep semantic-structural analysis.
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Read at arXiv cs.LG