Retinal IPA：用于多模态视网膜成像的迭代关键点对齐
Retinal IPA: Iterative KeyPoints Alignment for Multimodal Retinal Imaging
AI总结：
提出一种新颖的视网膜特征点对齐框架，通过整合基于关键点的分割任务并采用自监督方式训练，实现了跨模态鲁棒特征提取，显著提升了多模态视网膜图像的对齐性能。
摘要：
我们提出了一种新颖的视网膜特征点对齐框架，旨在学习跨模态特征，以增强多模态视网膜图像间的匹配与配准。我们的模型借鉴了以往基于学习的特征检测与描述方法的成功经验。为更好利用无标签数据并约束模型重现相关关键点，我们整合了基于关键点的分割任务。通过对同一图像的不同增强施加分割一致性，以自监督方式进行训练。通过引入关键点增强的自监督层，我们实现了跨模态的鲁棒特征提取。在两个公开数据集和一个内部数据集上的广泛评估表明，模态无关的视网膜特征对齐性能有显著提升。
英文摘要：
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}.
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。
