  Microscopic image segmentation is a challenging task, wherein the objective
is to assign semantic labels to each pixel in a given microscopic image. While
convolutional neural networks (CNNs) form the foundation of many existing
frameworks, they often struggle to explicitly capture long-range dependencies.
Although transformers were initially devised to address this issue using
self-attention, it has been proven that both local and global features are
crucial for addressing diverse challenges in microscopic images, including
variations in shape, size, appearance, and target region density. In this
paper, we introduce SA2-Net, an attention-guided method that leverages
multi-scale feature learning to effectively handle diverse structures within
microscopic images. Specifically, we propose scale-aware attention (SA2) module
designed to capture inherent variations in scales and shapes of microscopic
regions, such as cells, for accurate segmentation. This module incorporates
local attention at each level of multi-stage features, as well as global
attention across multiple resolutions. Furthermore, we address the issue of
blurred region boundaries (e.g., cell boundaries) by introducing a novel
upsampling strategy called the Adaptive Up-Attention (AuA) module. This module
enhances the discriminative ability for improved localization of microscopic
regions using an explicit attention mechanism. Extensive experiments on five
challenging datasets demonstrate the benefits of our SA2-Net model. Our source
code is publicly available at \url{https://github.com/mustansarfiaz/SA2-Net}.
