标题： SA-UNetv2：重新思考空间注意力U-Net用于视网膜血管分割 显示英文标题

标题： SA-UNetv2: Rethinking Spatial Attention U-Net for Retinal Vessel Segmentation

摘要： 视网膜血管分割对于糖尿病视网膜病变、高血压和神经退行性疾病的早期诊断至关重要。 尽管SA-UNet在瓶颈层引入了空间注意力，但它在跳跃连接中未充分利用注意力，并未解决严重的前景-背景不平衡问题。 我们提出了SA-UNetv2，这是一种轻量级模型，将跨尺度空间注意力注入所有跳跃连接，以增强多尺度特征融合，并采用加权二元交叉熵（BCE）加上马修斯相关系数（MCC）损失来提高对类别不平衡的鲁棒性。 在公开的DRIVE和STARE数据集上，SA-UNetv2仅需1.2MB内存和0.26M参数（少于SA-UNet的50%），并在592 x 592 x 3图像上实现1秒的CPU推理，证明了其在资源受限、仅CPU设置下的强大效率和可部署性。 显示英文摘要

摘要： Retinal vessel segmentation is essential for early diagnosis of diseases such as diabetic retinopathy, hypertension, and neurodegenerative disorders. Although SA-UNet introduces spatial attention in the bottleneck, it underuses attention in skip connections and does not address the severe foreground-background imbalance. We propose SA-UNetv2, a lightweight model that injects cross-scale spatial attention into all skip connections to strengthen multi-scale feature fusion and adopts a weighted Binary Cross-Entropy (BCE) plus Matthews Correlation Coefficient (MCC) loss to improve robustness to class imbalance. On the public DRIVE and STARE datasets, SA-UNetv2 achieves state-of-the-art performance with only 1.2MB memory and 0.26M parameters (less than 50% of SA-UNet), and 1 second CPU inference on 592 x 592 x 3 images, demonstrating strong efficiency and deployability in resource-constrained, CPU-only settings.