焦点注意力网络：优化生物医学图像分割的注意力
Focal Attention Networks: optimising attention for biomedical image segmentation
摘要：
近年来，人们越来越关注将注意力纳入深度学习架构中进行生物医学图像分割。注意力机制的模块化设计使得能够灵活地集成到卷积神经网络架构中，例如U-Net。注意力是否适合使用，使用什么类型的注意力，以及在网络中的何处纳入注意力模块，都是目前被忽视的重要考虑因素。在本文中，我们研究了焦点参数在调节注意力中的作用，揭示了损失函数和网络中注意力之间的联系。通过引入焦距惩罚项，我们扩展了统一焦点损失框架，以包括基于边界的损失。此外，我们开发了一个简单的和可解释的，数据集和模型特定的启发式算法，将焦点参数集成到挤压和激励块和注意力门中，在三个经过充分验证的生物医学成像数据集上使用较少的注意力模块实现最佳性能，这表明明智地使用注意力模块可以获得更好的性能和效率。
英文摘要：
In recent years, there has been increasing interest to incorporate attention into deep learning architectures for biomedical image segmentation. The modular design of attention mechanisms enables flexible integration into convolutional neural network architectures, such as the U-Net. Whether attention is appropriate to use, what type of attention to use, and where in the network to incorporate attention modules, are all important considerations that are currently overlooked. In this paper, we investigate the role of the Focal parameter in modulating attention, revealing a link between attention in loss functions and networks. By incorporating a Focal distance penalty term, we extend the Unified Focal loss framework to include boundary-based losses. Furthermore, we develop a simple and interpretable, dataset and model-specific heuristic to integrate the Focal parameter into the Squeeze-and-Excitation block and Attention Gate, achieving optimal performance with fewer number of attention modules on three well-validated biomedical imaging datasets, suggesting judicious use of attention modules results in better performance and efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
