PAM-UNet：在医学图像中移动对感兴趣区域的注意力
PAM-UNet: Shifting Attention on Region of Interest in Medical Images
AI总结：
本文提出PAM-UNet，通过倒置残差块保持轻量化，并利用逐层渐进式Luong注意力聚焦感兴趣区域，在LiTS 2017数据集上以极低计算量实现较高分割精度。
摘要：
计算机辅助分割方法可以协助医疗人员改善诊断结果。尽管UNet及其变体等近期进展显示出潜力，但它们面临一个关键挑战：在准确性与计算效率之间取得平衡。UNet中较浅的编码器架构往往难以捕获关键的空间特征，导致分割不准确且稀疏。为了解决这一局限，我们提出了一种新颖的基于渐进注意力的移动UNet（PAM-UNet）架构。PAM-UNet中的倒置残差（IR）块有助于保持轻量级框架，而逐层的渐进式Luong注意力（PLA）通过在合成过程中将注意力引导至感兴趣区域来促进精确分割。我们的方法同时优先考虑准确性和速度，在Liver Tumor Segmentation Benchmark（LiTS）2017数据集上取得了平均IoU为74.65、dice分数为82.87的可观平衡，同时仅需每秒1.32次浮点运算（FLOPS）。这些结果凸显了开发高效分割模型以加速人工智能在临床实践中应用的重要性。
英文摘要：
Computer-aided segmentation methods can assist medical personnel in improving diagnostic outcomes. While recent advancements like UNet and its variants have shown promise, they face a critical challenge: balancing accuracy with computational efficiency. Shallow encoder architectures in UNets often struggle to capture crucial spatial features, leading in inaccurate and sparse segmentation. To address this limitation, we propose a novel \underline{P}rogressive \underline{A}ttention based \underline{M}obile \underline{UNet} (\underline{PAM-UNet}) architecture. The inverted residual (IR) blocks in PAM-UNet help maintain a lightweight framework, while layerwise \textit{Progressive Luong Attention} ($\mathcal{PLA}$) promotes precise segmentation by directing attention toward regions of interest during synthesis. Our approach prioritizes both accuracy and speed, achieving a commendable balance with a mean IoU of 74.65 and a dice score of 82.87, while requiring only 1.32 floating-point operations per second (FLOPS) on the Liver Tumor Segmentation Benchmark (LiTS) 2017 dataset. These results highlight the importance of developing efficient segmentation models to accelerate the adoption of AI in clinical practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
