In recent years, deep learning technology has shown superior performance in different fields of medical image analysis. Some deep learning architectures have been proposed and used for computational pathology classification, segmentation, and detection tasks. Due to their simple, modular structure, most downstream applications still use ResNet and its variants as the backbone network. This paper proposes a modular group attention block that can capture feature dependencies in medical images in two independent dimensions: channel and space. By stacking these group attention blocks in ResNet-style, we obtain a new ResNet variant called ResGANet. The stacked ResGANet architecture has 1.51–3.47 times fewer parameters than the original ResNet and can be directly used for downstream medical image segmentation tasks. Many experiments show that the proposed ResGANet is superior to state-of-the-art backbone models in medical image classification tasks. Applying it to different segmentation networks can improve the baseline model in medical image segmentation tasks without changing the network architecture. We hope that this work provides a promising method for enhancing the feature representation of convolutional neural networks (CNNs) in the future.

近年来，深度学习技术在医学图像分析的不同领域表现出了优越的性能。一些深度学习架构已经被提出并用于计算病理学分类、分割和检测任务。由于其简单的模块化结构，大多数下游应用仍然使用 ResNet 及其变体作为主干网络。本文提出了一种模块化的群体注意力块，可以在两个独立的维度（通道和空间）捕获医学图像中的特征依赖性。通过以 ResNet 风格堆叠这些群体注意力块，我们获得了一个新的 ResNet 变体，称为 ResGANet。堆叠式ResGANet架构的参数比原始ResNet少1.51-3.47倍，可直接用于下游医学图像分割任务。许多实验表明，所提出的 ResGANet 在医学图像分类任务中优于最先进的骨干模型。将其应用于不同的分割网络可以在不改变网络架构的情况下改进医学图像分割任务中的基线模型。我们希望这项工作为未来增强卷积神经网络（CNN）的特征表示提供一种有前途的方法。