Barlow-Swin：一种基于Swin-Transformer的新型孪生分割架构
Barlow-Swin: Toward a novel siamese-based segmentation architecture using Swin-Transformers
AI总结：
提出Barlow-Swin，一种结合Swin Transformer编码器与U-Net解码器的轻量级端到端架构，利用Barlow Twins自监督预训练，在二值医学图像分割中实现高精度、低参数和快速推理，适合实时临床部署。
摘要：
医学图像分割是临床工作流程中的关键任务，尤其是在病理区域的检测和描绘方面。虽然像U-Net这样的卷积架构已成为此类任务的标准，但其有限的感受野限制了全局上下文建模。最近整合transformer的努力解决了这一问题，但往往导致模型深度大、计算成本高，不适合实时使用。在这项工作中，我们提出了一种新颖的端到端轻量级架构，专门用于实时二值医学图像分割。我们的模型结合了类似Swin Transformer的编码器和类似U-Net的解码器，通过跳跃连接保留空间细节，同时捕获上下文信息。与现有的Swin Transformer或U-Net设计不同，我们的架构明显更浅且具有竞争力的效率。为了提高编码器在不依赖大量标注数据的情况下学习有意义特征的能力，我们首先使用Barlow Twins（一种自监督学习方法）对其进行训练，该方法通过减少学习特征中的不必要重复，帮助模型关注重要模式。在预训练之后，我们针对特定任务对整体模型进行微调。在基准二值分割任务上的实验表明，我们的模型在显著减少参数数量和加快推理速度的同时，达到了具有竞争力的准确性，使其成为实时和资源受限临床环境中部署的实用替代方案。我们方法的代码可在GitHub仓库获取：https://github.com/mkianih/Barlow-Swin。
英文摘要：
Medical image segmentation is a critical task in clinical workflows, particularly for the detection and delineation of pathological regions. While convolutional architectures like U-Net have become standard for such tasks, their limited receptive field restricts global context modeling. Recent efforts integrating transformers have addressed this, but often result in deep, computationally expensive models unsuitable for real-time use. In this work, we present a novel end-to-end lightweight architecture designed specifically for real-time binary medical image segmentation. Our model combines a Swin Transformer-like encoder with a U-Net-like decoder, connected via skip pathways to preserve spatial detail while capturing contextual information. Unlike existing designs such as Swin Transformer or U-Net, our architecture is significantly shallower and competitively efficient. To improve the encoder's ability to learn meaningful features without relying on large amounts of labeled data, we first train it using Barlow Twins, a self-supervised learning method that helps the model focus on important patterns by reducing unnecessary repetition in the learned features. After this pretraining, we fine-tune the entire model for our specific task. Experiments on benchmark binary segmentation tasks demonstrate that our model achieves competitive accuracy with substantially reduced parameter count and faster inference, positioning it as a practical alternative for deployment in real-time and resource-limited clinical environments. The code for our method is available at Github repository: https://github.com/mkianih/Barlow-Swin.
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。
