Optimizing Universal Lesion Segmentation: State Space Model-Guided Hierarchical Networks with Feature Importance Adjustment

arXiv:2404.17235v1 Announce Type: cross
Abstract: Deep learning has revolutionized medical imaging by providing innovative solutions to complex healthcare challenges. Traditional models often struggle to dynamically adjust feature importance, resulting in suboptimal representation, particularly in tasks like semantic segmentation crucial for accurate structure delineation. Moreover, their static nature incurs high computational costs. To tackle these issues, we introduce Mamba-Ahnet, a novel integration of State Space Model (SSM) and Advanced Hierarchical Network (AHNet) within the MAMBA framework, specifically tailored for semantic segmentation …

abstract arxiv challenges cs.cv deep learning eess.iv feature healthcare hierarchical imaging importance medical medical imaging nature networks representation segmentation semantic solutions space state state space model struggle tasks type universal