用于视网膜血管分割的双编码特征滤波广义注意力UNET
Dual encoding feature filtering generalized attention UNET for retinal vessel segmentation
AI总结：
本文提出DEFFA-Unet，通过双编码器、特征滤波融合和注意力引导特征重构模块解决视网膜血管分割中数据不足、分布不平衡及特征提取不充分的问题，并在多个基准数据集上验证了其优越的泛化性能。
摘要：
视网膜血管分割对于诊断眼部疾病和心血管疾病至关重要。尽管Olaf Ronneberger于2015年提出的U-Net显著推动了该领域的发展，但训练数据有限、数据分布不平衡以及特征提取不充分等问题依然存在，阻碍了分割性能和模型的最优泛化能力。针对这些关键问题，本文提出了DEFFA-Unet，其特点是增加了一个额外的编码器来处理域不变预处理输入，从而改善更丰富的特征编码并增强模型泛化能力。开发了一个特征滤波融合模块，以确保精确的特征滤波和鲁棒的混合特征融合。针对任务特定的更高精度需求——其中假阳性代价极高，传统的跳跃连接被替换为注意力引导的特征重构融合模块。此外，提出了创新的数据增强和平衡方法，以应对数据稀缺和分布不平衡问题，进一步提升模型的鲁棒性和泛化能力。通过一套全面的评估指标，在四个基准数据集（DRIVE、CHASEDB1、STARE和HRF）以及一个SLO数据集（IOSTAR）上进行了广泛验证，证明了所提方法优于基线模型和当前最先进的模型。特别是，所提方法在交叉验证模型泛化方面显著优于对比方法。
英文摘要：
Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases. Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field, yet issues like limited training data, imbalance data distribution, and inadequate feature extraction persist, hindering both the segmentation performance and optimal model generalization. Addressing these critical issues, the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs, thereby improving both richer feature encoding and enhanced model generalization. A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion. In response to the task-specific need for higher precision where false positives are very costly, traditional skip connections are replaced with the attention-guided feature reconstructing fusion module. Additionally, innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance, further boosting the robustness and generalization of the model. With a comprehensive suite of evaluation metrics, extensive validations on four benchmark datasets (DRIVE, CHASEDB1, STARE, and HRF) and an SLO dataset (IOSTAR), demonstrate the proposed method's superiority over both baseline and state-of-the-art models. Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization.
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。
