  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.
