Graph Self-Supervised Learning for Endoscopic Image Matching
Manel Farhat Achraf Ben-Hamadou
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.