BRAIN-ADAPTER: ENHANCING NEUROLOGICAL DISORDER ANALYSIS WITH ADAPTER-TUNING MULTIMODAL LARGE LANGUAGE MODELS

Area of Science:

• Medical Imaging Analysis
• Artificial Intelligence in Healthcare
• Neuroscience

Background:

• Accurate diagnosis of brain disorders is essential for effective treatment.
• Multimodal Large Language Models (MLLMs) show promise for medical image interpretation.
• Existing methods often focus on 2D images and single modalities, neglecting valuable 3D spatial and cross-modal information.

Purpose of the Study:

• To develop a novel approach for integrating multimodal medical data, specifically 3D brain images and text, for improved diagnostic accuracy.
• To address the limitations of 2D-focused and single-modality methods in capturing comprehensive clinical information.

Main Methods:

• Proposed Brain-Adapter, a novel approach incorporating a lightweight bottleneck layer to learn and integrate new knowledge into pre-trained models.
• Utilized a Contrastive Language-Image Pre-training (CLIP) strategy to align multimodal data within a unified representation space.
• Focused on leveraging richer spatial information from 3D medical images.

Main Results:

• Demonstrated significant improvements in brain disorder diagnosis accuracy through the integration of multimodal data.
• Showcased the effectiveness of the lightweight bottleneck layer in capturing essential information with fewer parameters.
• Validated the approach's ability to align diverse data modalities into a unified representation.

Conclusions:

• Brain-Adapter effectively integrates multimodal data, enhancing diagnostic accuracy for brain disorders without substantial computational overhead.
• The proposed method holds significant potential for improving real-world clinical diagnostic workflows.
• Highlights the importance of leveraging both 3D spatial information and multimodal data in medical AI.