PAM-UNet: Shifting Attention on Region of Interest in Medical Images

arXiv:2405.01503v1 Announce Type: cross
Abstract: 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 …

abstract accuracy architectures arxiv attention challenge computational computer cs.cv diagnostic eess.iv efficiency encoder face features images improving medical segmentation spatial struggle type unet variants while