CrossModalNet: Multimodal Medical Segmentation with Guaranteed Cross-Modal Flow and Domain Adaptability
Keywords: biomedical imaging, transfer learning
TL;DR: We present a rigorous mathematical analysis of CrossModalNet, proving its universal approximation capabilities and deriving tight generalization bounds.
Abstract: The fusion of multimodal data in medical image segmentation has emerged as a critical frontier in biomedical research, promising unprecedented diagnostic precision and insights. However, the intricate challenge of effectively integrating diverse data streams while preserving their unique characteristics has persistently eluded comprehensive solutions. This study introduces CrossModalNet, a groundbreaking architecture that revolutionizes multimodal medical image segmentation through advanced mathematical frameworks and innovative domain adaptation techniques. We present a rigorous mathematical analysis of CrossModalNet, proving its universal approximation capabilities and deriving tight generalization bounds. Furthermore, we introduce the Cross-Modal Information Flow (CMIF) metric, providing theoretical justification for the progressive integration of multimodal information through the network layers. Our Joint Adversarial Domain Adaptation (JADA) framework addresses the critical issue of domain shift, simultaneously aligning marginal and conditional distributions while preserving topological structures. Extensive experiments on the MM-WHS dataset demonstrate CrossModalNet's superior performance. This work not only advances the field of medical image segmentation but also provides a robust theoretical foundation for future research in multimodal learning and domain adaptation across various biomedical applications.
Primary Area: transfer learning, meta learning, and lifelong learning
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Submission Number: 12965
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