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Abstract：
Retinal vessel segmentation is crucial for diagnosing and monitoring various eye diseases such as diabetic retinopathy, glaucoma, and hypertension. In this study, we examine how sharpness-aware minimization (SAM) can improve RF-UNet\'s generalization performance. RF-UNet is a novel model for retinal vessel segmentation. We focused our experiments on the digital retinal images for vessel extraction (DRIVE) dataset, which is a benchmark for retinal vessel segmentation, and our test results show that adding SAM to the training procedure leads to notable improvements. Compared to the non-SAM model (training loss of 0.45709 and validation loss of 0.40266), the SAM-trained RF-UNet model achieved a significant reduction in both training loss (0.094225) and validation loss (0.08053). Furthermore, compared to the non-SAM model (training accuracy of 0.90169 and validation accuracy of 0.93999), the SAM-trained model demonstrated higher training accuracy (0.96225) and validation accuracy (0.96821). Additionally, the model performed better in terms of sensitivity, specificity, AUC, and F1 score, indicating improved generalization to unseen data. Our results corroborate the notion that SAM facilitates the learning of flatter minima, thereby improving generalization, and are consistent with other research highlighting the advantages of advanced optimization methods. With wider implications for other medical imaging tasks, these results imply that SAM can successfully reduce overfitting and enhance the robustness of retinal vessel segmentation models. Prospective research avenues encompass verifying the model on vaster and more diverse datasets and investigating its practical implementation in real-world clinical situations.
摘要：
视网膜血管分割对于诊断和监测各种眼部疾病，如糖尿病性视网膜病变，青光眼,和高血压。在这项研究中,我们研究了锐度感知最小化(SAM)如何提高RF-UNet的泛化性能。RF-UNet是一种新的视网膜血管分割模型。我们的实验重点是血管提取的数字视网膜图像（DRIVE）数据集，这是视网膜血管分割的基准，我们的测试结果表明，在训练过程中添加SAM会带来显著的改进。与非SAM模型（训练损失为0.45709，验证损失为0.40266）相比，SAM训练的RF-UNet模型在训练损失(0.094225)和验证损失(0.08053)方面均实现了显著降低.此外，与非SAM模型(训练精度为0.90169，验证精度为0.93999)相比，SAM训练模型显示出更高的训练准确度(0.96225)和验证准确度(0.96821).此外,模型在灵敏度方面表现更好，特异性，AUC,和F1得分，表明改进了对看不见的数据的泛化。我们的结果证实了SAM有助于学习更平坦的最小值的观点，从而提高泛化，并与其他强调先进优化方法优势的研究相一致。对其他医学成像任务有更广泛的影响，这些结果表明SAM可以成功地减少过拟合并增强视网膜血管分割模型的鲁棒性。前瞻性研究途径包括在更大和更多样化的数据集上验证模型，并研究其在现实世界临床情况下的实际实施。
