Abstract
Over the past decade, generative models have achieved significant success in enhancement fundus this http URL, the evaluation of these models still presents a considerable challenge. A comprehensive evaluation benchmark for fundus image enhancement is indispensable for three main reasons: 1) The existing denoising metrics (e.g., PSNR, SSIM) are hardly to extend to downstream real-world clinical research (e.g., Vessel morphology consistency). 2) There is a lack of comprehensive evaluation for both paired and unpaired enhancement methods, along with the need for expert protocols to accurately assess clinical value. 3) An ideal evaluation system should provide insights to inform future developments of fundus image enhancement. To this end, we propose a novel comprehensive benchmark, EyeBench, to provide insights that align enhancement models with clinical needs, offering a foundation for future work to improve the clinical relevance and applicability of generative models for fundus image enhancement. EyeBench has three appealing properties: 1) multi-dimensional clinical alignment downstream evaluation: In addition to evaluating the enhancement task, we provide several clinically significant downstream tasks for fundus images, including vessel segmentation, DR grading, denoising generalization, and lesion segmentation. 2) Medical expert-guided evaluation design: We introduce a novel dataset that promote comprehensive and fair comparisons between paired and unpaired methods and includes a manual evaluation protocol by medical experts. 3) Valuable insights: Our benchmark study provides a comprehensive and rigorous evaluation of existing methods across different downstream tasks, assisting medical experts in making informed choices. Additionally, we offer further analysis of the challenges faced by existing methods. The code is available at \url{this https URL}
Abstract (translated)
过去十年里，生成模型在眼底图像增强方面取得了显著的成功。然而，在评估这些模型时仍然面临着重大挑战。一个全面的基准测试对于眼底图像增强来说是必不可少的，原因有三：1）现有的去噪指标（如PSNR、SSIM等）难以扩展到下游的实际临床研究中（例如血管形态的一致性）。2）目前缺乏对配对和非配对增强方法进行全面评估的方法，并且需要专家制定准确衡量临床价值的标准。3）理想的评估系统应该能为未来的眼底图像增强生成模型的发展提供见解。 为此，我们提出了一个新型的全面基准测试EyeBench，旨在使增强模型更好地与临床需求相匹配，为将来改进眼底图像增强生成模型的临床相关性和应用性奠定基础。EyeBench具有三大优势： 1. 多维度下游任务的临床对齐评估：除了对增强任务进行评价外，我们还提供了多个重要的下游任务，包括血管分割、糖尿病视网膜病变分级、去噪泛化和病灶分割等。 2. 医学专家引导下的评估设计：引入了一个新的数据集以促进配对与非配对方法之间的全面且公平比较，并包括由医学专家制定的手动评价协议。 3. 有价值的见解：我们的基准测试研究提供了现有方法在不同下游任务中的综合和严格的评估，帮助医学专家做出知情选择。此外，我们还分析了当前方法面临的挑战。 代码可以在\url{此链接}获得。
URL
https://arxiv.org/abs/2502.14260
