ConKeD：面向基于关键点的视网膜图像配准的多视图对比描述符学习
ConKeD: Multiview contrastive descriptor learning for keypoint-based retinal image registration
AI总结：
本文提出ConKeD方法，通过多正例多负例对比学习策略从有限数据中学习高质量描述符，并结合血管分叉与交叉关键点，在视网膜图像配准中达到与最先进方法相当的性能且所需训练样本和关键点更少。
摘要：
视网膜图像配准因其在医疗实践中的广泛应用而至关重要。在此背景下，我们提出了ConKeD，一种用于学习视网膜图像配准描述符的新型深度学习方法。与当前的配准方法不同，我们的方法采用了一种新颖的多正例多负例对比学习策略，能够利用可用训练样本中的额外信息。这使得从有限的训练数据中学习高质量描述符成为可能。为了训练和评估ConKeD，我们将这些描述符与领域特定的关键点（特别是使用深度神经网络检测到的血管分叉点和交叉点）相结合。我们的实验结果表明，这种新颖的多正例多负例策略具有明显优势，其性能优于广泛使用的三元组损失技术（单正例单负例）以及单正例多负例的替代方案。此外，ConKeD与领域特定关键点的结合取得了与视网膜图像配准的最先进方法相当的结果，同时提供了重要优势，例如避免预处理、使用更少的训练样本以及需要更少的检测关键点等。因此，ConKeD在促进基于深度学习的视网膜图像配准方法的开发和应用方面展现出巨大潜力。
英文摘要：
Retinal image registration is of utmost importance due to its wide applications in medical practice. In this context, we propose ConKeD, a novel deep learning approach to learn descriptors for retinal image registration. In contrast to current registration methods, our approach employs a novel multi-positive multi-negative contrastive learning strategy that enables the utilization of additional information from the available training samples. This makes it possible to learn high quality descriptors from limited training data. To train and evaluate ConKeD, we combine these descriptors with domain-specific keypoints, particularly blood vessel bifurcations and crossovers, that are detected using a deep neural network. Our experimental results demonstrate the benefits of the novel multi-positive multi-negative strategy, as it outperforms the widely used triplet loss technique (single-positive and single-negative) as well as the single-positive multi-negative alternative. Additionally, the combination of ConKeD with the domain-specific keypoints produces comparable results to the state-of-the-art methods for retinal image registration, while offering important advantages such as avoiding pre-processing, utilizing fewer training samples, and requiring fewer detected keypoints, among others. Therefore, ConKeD shows a promising potential towards facilitating the development and application of deep learning-based methods for retinal image registration.
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。
