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 in medical imaging.Mamba-Ahnet combines SSM's feature extraction and comprehension with AHNet's attention mechanisms and image reconstruction, aiming to enhance segmentation accuracy and robustness. By dissecting images into patches and refining feature comprehension through self-attention mechanisms, the approach significantly improves feature resolution. Integration of AHNet into the MAMBA framework further enhances segmentation performance by selectively amplifying informative regions and facilitating the learning of rich hierarchical representations. Evaluation on the Universal Lesion Segmentation dataset demonstrates superior performance compared to state-of-the-art techniques, with notable metrics such as a Dice similarity coefficient of approximately 98% and an Intersection over Union of about 83%. These results underscore the potential of our methodology to enhance diagnostic accuracy, treatment planning, and ultimately, patient outcomes in clinical practice. By addressing the limitations of traditional models and leveraging the power of deep learning, our approach represents a significant step forward in advancing medical imaging technology.

翻译：深度学习通过为复杂医疗挑战提供创新解决方案，彻底改变了医学影像领域。传统模型往往难以动态调整特征重要性，导致表征能力欠佳，尤其在语义分割（准确描绘解剖结构的关键任务）中表现尤为突出。此外，其静态特性带来高昂的计算成本。为解决这些问题，我们提出Mamba-Ahnet——在MAMBA框架内创新融合状态空间模型与高级分层网络，专为医学影像语义分割设计。Mamba-Ahnet将SSM的特征提取与理解能力同AHNet的注意力机制及图像重建功能相结合，旨在提升分割精度与鲁棒性。通过将图像拆解为图像块，并利用自注意力机制优化特征理解，该方法显著提升了特征分辨率。将AHNet集成至MAMBA框架后，通过选择性增强信息丰富区域并促进分层表征学习，进一步提升了分割性能。在通用病灶分割数据集上的评估表明，其性能优于现有最先进技术，关键指标包括Dice相似系数约达98%、交并比约达83%。这些结果凸显了该方法在提升诊断准确性、优化治疗规划乃至改善临床患者预后方面的潜力。通过克服传统模型局限并发挥深度学习优势，我们的方法标志着医学影像技术的重要进步。