Accurate feature matching and correspondence in endoscopic images play a crucial role in various clinical applications, including patient follow-up and rapid anomaly localization through panoramic image generation. However, developing robust and accurate feature matching techniques faces challenges due to the lack of discriminative texture and significant variability between patients. To address these limitations, we propose a novel self-supervised approach that combines Convolutional Neural Networks for capturing local visual appearance and attention-based Graph Neural Networks for modeling spatial relationships between key-points. Our approach is trained in a fully self-supervised scheme without the need for labeled data. Our approach outperforms state-of-the-art handcrafted and deep learning-based methods, demonstrating exceptional performance in terms of precision rate (1) and matching score (99.3%). We also provide code and materials related to this work, which can be accessed at https://github.com/abenhamadou/graph-self-supervised-learning-for-endoscopic-image-matching.

翻译：内窥镜图像中的精确特征匹配与对应在各种临床应用中发挥着关键作用，包括患者随访和通过全景图像生成快速定位异常区域。然而，由于缺乏区分性纹理以及患者间的显著差异性，开发稳健且精确的特征匹配技术面临挑战。为克服这些限制，我们提出了一种新颖的自监督方法，该方法结合了卷积神经网络以捕捉局部视觉外观，以及基于注意力机制的图神经网络来建模关键点之间的空间关系。我们的方法在完全自监督框架下进行训练，无需标注数据。该方法超越了最先进的手工设计方法和基于深度学习的方法，在精确率（1）和匹配分数（99.3%）方面展现出卓越性能。我们还提供了与本研究相关的代码和材料，可通过 https://github.com/abenhamadou/graph-self-supervised-learning-for-endoscopic-image-matching 访问。