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}.

翻译：显微图像分割是一项具有挑战性的任务，其目标是为给定显微图像中的每个像素分配语义标签。虽然卷积神经网络（CNN）构成了许多现有框架的基础，但它们通常难以显式地捕捉长距离依赖性。尽管Transformer最初被设计用于通过自注意力解决这一问题，但已有研究证明，局部和全局特征对于应对显微图像中的多样化挑战（包括形状、大小、外观以及目标区域密度的变化）至关重要。本文提出了一种注意力引导方法SA2-Net，该方法利用多尺度特征学习有效处理显微图像中的多样结构。具体而言，我们设计了一个尺度感知注意力（SA2）模块，旨在捕捉显微区域（如细胞）在尺度和形状上的固有变化，以实现精确分割。该模块在多层特征的每一层级整合了局部注意力，并融入了跨多分辨率的全局注意力。此外，我们通过引入一种名为自适应上采样注意力（AuA）模块的新型上采样策略，解决了区域边界（例如细胞边界）模糊的问题。该模块利用显式注意力机制增强了判别能力，以改进显微区域的定位。在五个具有挑战性的数据集上的大量实验证明了我们SA2-Net模型的优势。我们的源代码已在\url{https://github.com/mustansarfiaz/SA2-Net}上公开。