Brain Foundation Models with Hypergraph Dynamic Adapter for Brain Disease Analysis (2505.00627v1)

Abstract: Brain diseases, such as Alzheimer's disease and brain tumors, present profound challenges due to their complexity and societal impact. Recent advancements in brain foundation models have shown significant promise in addressing a range of brain-related tasks. However, current brain foundation models are limited by task and data homogeneity, restricted generalization beyond segmentation or classification, and inefficient adaptation to diverse clinical tasks. In this work, we propose SAM-Brain3D, a brain-specific foundation model trained on over 66,000 brain image-label pairs across 14 MRI sub-modalities, and Hypergraph Dynamic Adapter (HyDA), a lightweight adapter for efficient and effective downstream adaptation. SAM-Brain3D captures detailed brain-specific anatomical and modality priors for segmenting diverse brain targets and broader downstream tasks. HyDA leverages hypergraphs to fuse complementary multi-modal data and dynamically generate patient-specific convolutional kernels for multi-scale feature fusion and personalized patient-wise adaptation. Together, our framework excels across a broad spectrum of brain disease segmentation and classification tasks. Extensive experiments demonstrate that our method consistently outperforms existing state-of-the-art approaches, offering a new paradigm for brain disease analysis through multi-modal, multi-scale, and dynamic foundation modeling.

• The paper proposes a novel framework combining a brain-specific foundation model (SAM-Brain3D) with a dynamic adapter (HyDA) for enhanced brain disease analysis.
• SAM-Brain3D is trained on a large dataset of over 66,000 brain images, enabling robust segmentation across diverse brain targets and MRI modalities.
• The Hypergraph Dynamic Adapter (HyDA) dynamically integrates heterogeneous multi-modal clinical data like imaging and genetics, facilitating personalized adaptation and analysis.

Brain Foundation Models with Hypergraph Dynamic Adapter for Brain Disease Analysis

The presented paper addresses the challenges inherent in brain disease analysis by proposing an innovative framework combining brain-specific foundation models with dynamic adaptation techniques. The paper introduces two primary contributions: SAM-Brain3D, a brain-specific foundation model, and Hypergraph Dynamic Adapter (HyDA), a dynamic adaptation tool for integrating heterogeneous clinical data.

SAM-Brain3D: A Robust Brain Foundation Model

SAM-Brain3D is developed to address limitations in current medical imaging models, such as their confinement to particular tasks like segmentation or classification and a lack of adaptability to diverse clinical requirements. The model is trained using over 66,000 brain image-label pairs across 14 different MRI sub-modalities, ensuring it adeptly captures anatomical and modality-specific details of the brain. This extensive training dataset empowers SAM-Brain3D to segment a variety of brain targets, beyond the capabilities of existing models.

Hypergraph Dynamic Adapter (HyDA)

HyDA is proposed as a lightweight, flexible adapter that enhances the SAM-Brain3D model's ability to process and integrate multi-modal clinical data. The adapter employs hypergraphs to dynamically merge complementary data types and generate subject-specific convolutional kernels, facilitating multi-scale feature fusion and personalized adaptation. The dynamic adaptation capability of HyDA is particularly significant for integrating different types of imaging data (MRI, PET) along with non-imaging data such as genetic and demographic information, which is crucial for personalized brain disease analysis.

Experimental Validation and Comparative Analysis

The researchers evaluated their approach through extensive experiments, demonstrating that the SAM-Brain3D with HyDA consistently outperforms current state-of-the-art methodologies across a wide array of tasks in brain disease segmentation and classification. Experimental results show notable improvements in segmentation accuracy on tasks involving both seen and unseen brain structures, underscoring the model's robust generalizability.

Implications for Future AI Developments

The implications of this research are substantial both from a practical and theoretical standpoint. Practically, the approach presents a viable path for deploying adaptable and highly accurate AI models in clinical settings. Theoretically, it suggests a framework for effectively utilizing multi-modal, multi-scale data, which could be expanded to other regions of the human body or to entirely different domains. Future research may focus on enhancing the scalability and applicability of these techniques to encompass wider medical and diagnostic challenges, potentially incorporating other emerging AI technologies in medical imaging and diagnostics.

In conclusion, the fusion of SAM-Brain3D and HyDA represents a significant step forward for brain disease analysis, providing a foundational model that is both deeply specialized and broadly adaptable across various clinical tasks. The model's success in integrating complex data sources with dynamic adaptation paves the way for more advanced and versatile AI-driven diagnostic tools in healthcare.