Biofusionnet: Real-time Multimodal Biomedical Image and Signal Synthesis Via Predictive Encoding and Cross-resolution Attention Mechanisms
Year: 2025
Type: article
Abstract: The increasing complexity of clinical scenarios necessitates advanced biomedical imaging and sensing frameworks that address real-time challenges, such as multimodal data integration, noise suppression, and accurate decision support. Current solutions often fall short in adapting to diverse imaging environments, real-time constraints, and providing interpretable outputs, limiting their utility in dynamic clinical workflows. To address these limitations, we propose a novel, end-to-end system integrating multiple advanced methods for biomedical imaging and sensing process. Our Adaptive Multimodal Fusion Network (AMFN) combines multimodal imaging data (MRI, CT, Ultrasound, etc.), dynamically weighting modalities using an adaptive fusion module to generate enhanced composite images. Self-supervised learning further reconstructs missing modalities for robust diagnostic accuracy. For real-time insights, the Lightweight Dynamic Segmentation (LDS) module uses a transformer-based attention mechanism in a lightweight neural architecture to segment critical regions of interest with high precision, even on portable edge devices. Noise and artifact suppression are managed by the Contextual Noise Elimination Module (CNEM), which employs convolutional autoencoders and attention-guided filters to enhance image clarity without sacrificing key clinical details. The Predictive Augmented Imaging (PAI) module leverages generative adversarial networks to predict and augment low-quality or incomplete imaging data, ensuring comprehensive results. Finally, the Explainable Imaging Support System (EISS) delivers transparent, interpretable clinical predictions with saliency maps and uncertainty quantification to bolster clinician trust. This integrated framework improves diagnostic accuracy, supports real-time adaptability, and ensures robust usability in resource-limited settings, setting a new benchmark for biomedical imaging in real-world clinical applications
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7118184499
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