  We propose a novel framework for retinal feature point alignment, designed
for learning cross-modality features to enhance matching and registration
across multi-modality retinal images. Our model draws on the success of
previous learning-based feature detection and description methods. To better
leverage unlabeled data and constrain the model to reproduce relevant
keypoints, we integrate a keypoint-based segmentation task. It is trained in a
self-supervised manner by enforcing segmentation consistency between different
augmentations of the same image. By incorporating a keypoint augmented
self-supervised layer, we achieve robust feature extraction across modalities.
Extensive evaluation on two public datasets and one in-house dataset
demonstrates significant improvements in performance for modality-agnostic
retinal feature alignment. Our code and model weights are publicly available at
\url{https://github.com/MedICL-VU/RetinaIPA}.
