Retinal IPA: Iterative KeyPoints Alignment for Multimodal Retinal Imaging
<|reference_start|>Retinal IPA: Iterative KeyPoints Alignment for Multimodal Retinal Imaging: 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}.<|reference_end|>
@article{wang2024retinal,
    title={Retinal IPA: Iterative KeyPoints Alignment for Multimodal Retinal
  Imaging},
    author={Jiacheng Wang, Hao Li, Dewei Hu, Rui Xu, Xing Yao, Yuankai K. Tao,
  Ipek Oguz},
    journal={arXiv preprint arXiv:2407.18362},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18362},
    primaryClass={eess.IV cs.CV cs.LG}
}