AI DRIVEN NEUROVISION-FM: A SELF-SUPERVISED MULTIMODAL FOUNDATION MODEL FOR GENERALIZABLE BRAIN MRI ANALYSIS

Abstract

Brain magnetic resonance imaging (MRI) analysis is increasingly supported by artificial intelligence, yet many existing models remain dependent on task-specific supervised learning, large expert-labeled datasets, single-modality inputs, and narrowly sampled institutional cohorts. These limitations can reduce generalization across scanners, acquisition protocols, hospitals, and patient populations. This paper proposes NeuroVision-FM, a self-supervised multimodal foundation-model framework for generalizable brain MRI analysis. The framework combines self-supervised representation learning, CNN based local feature extraction, Transformer-based global contextual modeling, multimodal clinical feature encoding, cross-modal attention, adaptive feature fusion, uncertainty estimation, and explainable artificial intelligence. The design supports downstream classification, lesion localization/segmentation, and risk-oriented assessment while maintaining patient-level separation during evaluation. A rigorous evaluation protocol is specified using public brain-MRI datasets, patient-level partitioning, external validation, class-balanced metrics, calibration analysis, robustness testing, ablation studies, and statistical confidence intervals. Explainability is assessed through image-region attribution and structured feature importance, with clinical interpretability reviewed against expert-annotated or clinically meaningful regions where feasible. The manuscript deliberately distinguishes the proposed architecture from a fully established clinical foundation model: numerical performance values and figures must be generated from real experiments and must not be represented as measured evidence until validated. NeuroVision-FM is positioned as a reproducible research framework for studying whether self-supervised multimodal representations can improve cross-domain robustness, label efficiency, interpretability, and transferability in brain MRI analysis. Brain magnetic resonance imaging (MRI) analysis has increasingly benefited from artificial intelligence (AI) and deep learning (DL); however, many existing approaches remain constrained by task-specific supervised learning, limited expert annotations, single-modality inputs, institutional domain bias, and insufficient interpretability. These limitations can substantially affect the reliability and generalizability of AI systems when they are transferred across scanners, acquisition protocols, healthcare institutions, and patient populations. To address these challenges, this study proposes NeuroVision-FM, a self-supervised multimodal foundation-model framework for generalizable brain MRI analysis. The proposed architecture integrates self-supervised representation learning with complementary convolutional neural network (CNN) and Vision Transformer (ViT) encoders to capture local anatomical patterns and long-range contextual dependencies. A multimodal representation layer incorporates structured clinical information and, where available, electronic health records and medical text through cross-modal attention and adaptive feature fusion. The framework further incorporates uncertainty estimation and explainable artificial intelligence (XAI) to improve transparency, reliability, and clinical interpretability. The proposed learning pipeline consists of patient-level data partitioning, MRI quality control, modality harmonization, preprocessing and augmentation, self-supervised pretraining, multimodal representation alignment, adaptive fusion, task specific fine-tuning, uncertainty estimation, and explanation generation. The framework is designed to support multiple downstream applications, including brain-tumor classification, lesion localization and segmentation, disease characterization, and risk-oriented prediction. Evaluation is designed around rigorous patient-level validation using accuracy, precision, sensitivity, specificity, F1-score, ROC-AUC, PR-AUC, calibration error, computational efficiency, and robustness under distribution shift. For segmentation tasks, Dice similarity coefficient, Jaccard index/IoU, Hausdorff distance, and sensitivity are considered. Statistical confidence intervals, ablation experiments, external validation, missing-modality analysis, and subgroup evaluation are incorporated to assess the reliability and transferability of the learned representations. A central objective of NeuroVision-FM is to investigate whether self-supervised multimodal learning can reduce dependence on expensive annotations while improving cross-domain generalization and clinical interpretability. The proposed framework is therefore positioned not merely as a task-specific diagnostic classifier but as a transferable representation-learning architecture capable of adaptation to diverse brain-MRI analysis tasks. Importantly, all quantitative performance values and clinical claims must be established through reproducible experiments on independent datasets and should not be inferred from the proposed architecture alone. The study provides a methodological foundation for developing privacy-conscious, generalizable, interpretable, and clinically responsible medical foundation models for next-generation neuroimaging applications.