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.

翻译：计算机辅助分割方法可帮助医疗人员改善诊断结果。尽管最近UNet及其变体等进展显示出潜力，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中的浅层编码器架构往往难以捕捉关键的空间特征，导致分割不准确且稀疏。为解决这一限制，我们提出了一种新颖的基于渐进式注意力的轻量级UNet架构（PAM-UNet）。PAM-UNet中的倒残差块有助于保持轻量级框架，而逐层的渐进式Luong注意力（$\mathcal{PLA}$）通过在合成过程中将注意力引向感兴趣区域来促进精确分割。我们的方法兼顾准确性与速度，在肝脏肿瘤分割基准（LiTS）2017数据集上实现了平均交并比74.65、Dice系数82.87的令人满意的平衡，同时仅需每秒1.32次浮点运算。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。