CATFA-Net：一种用于准确医学图像分割的跨卷积方法
CATFA-Net: A Trans-Convolutional Approach for Accurate Medical Image Segmentation
AI总结：
CATFA-Net通过引入Context Addition Attention和Cross-Channel Attention机制，提升医学图像分割的准确性和效率，实现高效且鲁棒的二分类分割。
摘要：
卷积块在医学图像分割中发挥了关键作用，但其无法有效捕捉长距离依赖性限制了性能。基于Transformer的架构通过注意力机制建模全局上下文并创建具有表达性的特征表示。本文提出CATFA-Net，一种新颖且高效的分割框架，通过轻量级卷积解码器主干和层次化混合编码器架构，结合新的Context Addition Attention机制和Cross-Channel Attention机制，实现高精度分割并降低计算成本。在五个公开数据集上的评估显示，CATFA-Net在准确性和效率上均优于现有方法，其在GLaS和ISIC 2018上的Dice分数分别达到94.48%和91.55%。鲁棒性测试和外部验证进一步证明其在二分类任务中的强泛化能力。
英文摘要：
Convolutional blocks have played a crucial role in advancing medical image segmentation by excelling in dense prediction tasks. However, their inability to effectively capture long-range dependencies has limited their performance. Transformer-based architectures, leveraging attention mechanisms, address this limitation by modeling global context and creating expressive feature representations. Recent research has explored this potential by introducing hybrid frameworks that combine transformer encoders with convolutional decoders. Despite their advantages, these approaches face challenges such as limited inductive bias, high computational cost, and reduced robustness to data variability. To overcome these issues, this study introduces CATFA-Net, a novel and efficient segmentation framework designed to produce high-quality segmentation masks while reducing computational costs and increasing inference speed. CATFA-Net employs a hierarchical hybrid encoder architecture with a lightweight convolutional decoder backbone. Its transformer-based encoder uses a new Context Addition Attention mechanism that captures inter-image dependencies without the quadratic complexity of standard attention mechanisms. Features from the transformer branch are fused with those from the convolutional branch through a proposed Cross-Channel Attention mechanism, which helps retain spatial and channel information during downsampling. Additionally, a Spatial Fusion Attention mechanism in the decoder refines features while reducing background noise ambiguity. Extensive evaluations on five publicly available datasets show that CATFA-Net outperforms existing methods in accuracy and efficiency. The framework sets new state-of-the-art Dice scores on GLaS (94.48%) and ISIC 2018 (91.55%). Robustness tests and external validation further demonstrate its strong ability to generalize in binary segmentation tasks.
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。
