无需标注数据，直接训练通用视网膜图像配准描述子
Unsupervised training of keypoint-agnostic descriptors for flexible retinal image registration
- 设计无关键点依赖的无监督描述子学习方法
- 在公开数据集上达到与有监督方法相当的配准精度
- 兼容多种关键点检测器，适合医疗图像配准研究者
当前彩色眼底图像配准方法受限于标注数据不足，尤其在医学领域更为突出，这推动了无监督学习的应用。本文提出一种新型无监督描述子学习方法，不依赖关键点检测，使生成的描述子网络在配准推理中对关键点检测器具有无关性。通过在基准公开眼底图像配准数据集上进行广泛且全面的对比验证，并测试了多种不同类型的关键点检测器（包括部分新提出的检测器），结果表明：所提方法在配准精度上与有监督方法无显著差异，且性能不受关键点检测器影响。该工作为无监督学习在医学领域的应用迈出重要一步。
原文摘要 · Abstract (English)
Current color fundus image registration approaches are limited, among other things, by the lack of labeled data, which is even more significant in the medical domain, motivating the use of unsupervised learning. Therefore, in this work, we develop a novel unsupervised descriptor learning method that does not rely on keypoint detection. This enables the resulting descriptor network to be agnostic to the keypoint detector used during the registration inference. To validate this approach, we perform an extensive and comprehensive comparison on the reference public retinal image registration dataset. Additionally, we test our method with multiple keypoint detectors of varied nature, even proposing some novel ones. Our results demonstrate that the proposed approach offers accurate registration, not incurring in any performance loss versus supervised methods. Additionally, it demonstrates accurate performance regardless of the keypoint detector used. Thus, this work represents a notable step towards leveraging unsupervised learning in the medical domain.
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