重新思考注意力门控与混合双金字塔Transformer-CNN在医学影像通用分割中的应用
Rethinking Attention Gated with Hybrid Dual Pyramid Transformer-CNN for Generalized Segmentation in Medical Imaging
AI总结：
提出混合CNN-Transformer架构PAG-TransYnet，通过双金字塔编码器、PVT transformer与双注意力门控机制融合多尺度特征，在多类医学影像分割任务中达到SOTA并展现强泛化能力。
摘要：
受Transformers在计算机视觉中取得成功的启发，Transformers已被广泛研究用于医学影像分割。然而，大多数Transformer架构将最新的Transformer架构用作编码器，或作为与CNN编码器并行的编码器。在本文中，我们引入了一种新颖的混合CNN-Transformer分割架构（PAG-TransYnet），旨在高效构建强大的CNN-Transformer编码器。我们的方法利用了双金字塔混合编码器内的注意力门。该方法的贡献可总结为三个关键方面：（i）利用金字塔输入来突出不同尺度上的显著特征，（ii）结合PVT transformer以捕获跨各种分辨率的远程依赖关系，（iii）实现双注意力门控机制，以有效融合来自CNN和Transformer分支的显著特征。通过在不同分割任务上进行全面评估，包括：腹部多器官分割、感染分割（Covid-19和骨转移）、显微组织分割（腺体和细胞核）。所提出的方法展示了最先进的性能，并表现出卓越的泛化能力。这项研究代表了在解决医学影像应用中对高效且适应性强的分割解决方案的迫切需求方面取得的重大进展。
英文摘要：
Inspired by the success of Transformers in Computer vision, Transformers have been widely investigated for medical imaging segmentation. However, most of Transformer architecture are using the recent transformer architectures as encoder or as parallel encoder with the CNN encoder. In this paper, we introduce a novel hybrid CNN-Transformer segmentation architecture (PAG-TransYnet) designed for efficiently building a strong CNN-Transformer encoder. Our approach exploits attention gates within a Dual Pyramid hybrid encoder. The contributions of this methodology can be summarized into three key aspects: (i) the utilization of Pyramid input for highlighting the prominent features at different scales, (ii) the incorporation of a PVT transformer to capture long-range dependencies across various resolutions, and (iii) the implementation of a Dual-Attention Gate mechanism for effectively fusing prominent features from both CNN and Transformer branches. Through comprehensive evaluation across different segmentation tasks including: abdominal multi-organs segmentation, infection segmentation (Covid-19 and Bone Metastasis), microscopic tissues segmentation (Gland and Nucleus). The proposed approach demonstrates state-of-the-art performance and exhibits remarkable generalization capabilities. This research represents a significant advancement towards addressing the pressing need for efficient and adaptable segmentation solutions in medical imaging applications.
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。
