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 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.
Abstract (translated)
深度学习通过提供解决复杂医疗挑战的创新解决方案，彻底颠覆了医学影像学。传统的模型通常很难动态地调整特征重要性，导致效果不佳，特别是在对准确结构描绘至关重要的任务中，如语义分割。此外，它们的静态特性还导致高计算成本。为了应对这些挑战，我们引入了Mamba-Ahnet，一种将状态空间模型（SSM）和高级层次网络（AHNet）相结合的MAMBA框架，特别针对医学影像中的语义分割进行优化。Mamba-Ahnet将SSM的特征提取和理解与AHNet的注意机制和图像重建相结合，旨在提高分割准确性和稳健性。通过将图像切分为补丁并通过自注意力机制优化特征理解，该方法显著提高了特征分辨率。将AHNet融入MAMBA框架进一步提高了分割性能，通过选择性地放大有信息区域并促进丰富的层次表示学习，实现了更好的分割效果。在统一病变分割数据集上进行的评估显示，与最先进的 techniques相比，其性能具有卓越的优势，重要指标如Dice相似性系数约为98%，交集与并集之比约为83%。这些结果强调了我们在临床实践中提高诊断准确度、治疗规划和患者预后的潜力。通过解决传统模型的局限性并利用深度学习的优势，我们的方法在推动医学影像技术发展方面取得了显著的进展。
URL
https://arxiv.org/abs/2404.17235
