# Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching

arXiv: https://arxiv.org/abs/2207.07932 · HTML: https://mlsanity.com/paper/2207.07932/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 16 Jul 2022.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [R2D2: Repeatable and Reliable Detector and Descriptor](https://mlsanity.com/paper/1906.06195/) (2019)
- [COTR: Correspondence Transformer for Matching Across Images](https://mlsanity.com/paper/2103.14167/) (2021)
- [Neighbourhood Consensus Networks](https://mlsanity.com/paper/1810.10510/) (2018)
- [SOSNet: Second Order Similarity Regularization for Local Descriptor Learning](https://mlsanity.com/paper/1904.05019/) (2019)
- [Learning Accurate Dense Correspondences and When to Trust Them](https://mlsanity.com/paper/2101.01710/) (2021)
- [Learn to Segment Retinal Lesions and Beyond](https://mlsanity.com/paper/1912.11619/) (2019)
- [GLAMpoints: Greedily Learned Accurate Match points](https://mlsanity.com/paper/1908.06812/) (2019)

## Built on by (most cited first, 27 of 27)

- [MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training](https://mlsanity.com/paper/2501.07556/) (2025)
- [ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Images](https://mlsanity.com/paper/2406.14794/) (2024)
- [Reverse Knowledge Distillation: Training a Large Model using a Small One for Retinal Image Matching on Limited Data](https://mlsanity.com/paper/2307.10698/) (2023)
- [Learning Intra-view and Cross-view Geometric Knowledge for Stereo Matching](https://mlsanity.com/paper/2402.19270/) (2024)
- [RetinaRegNet: A Zero-Shot Approach for Retinal Image Registration](https://mlsanity.com/paper/2404.16017/) (2024)
- [Deep Learning Reforms Image Matching: A Survey and Outlook](https://mlsanity.com/paper/2506.04619/) (2025)
- [Progressive Retinal Image Registration via Global and Local Deformable Transformations](https://mlsanity.com/paper/2409.01068/) (2024)
- [MIFNet: Learning Modality-Invariant Features for Generalizable Multimodal Image Matching](https://mlsanity.com/paper/2501.11299/) (2025)
- [MapGlue: Multimodal Remote Sensing Image Matching](https://mlsanity.com/paper/2503.16185/) (2025)
- [ConKeD: Multiview contrastive descriptor learning for keypoint-based retinal image registration](https://mlsanity.com/paper/2401.05901/) (2024)
- [Medical Image Registration and Its Application in Retinal Images: A Review](https://mlsanity.com/paper/2403.16502/) (2024)
- [Retinal IPA: Iterative KeyPoints Alignment for Multimodal Retinal Imaging](https://mlsanity.com/paper/2407.18362/) (2024)
- [Unsupervised Deformable Image Registration with Structural Nonparametric Smoothing](https://mlsanity.com/paper/2506.10813/) (2025)
- [A 3D Cross-modal Keypoint Descriptor for MR-US Matching and Registration](https://mlsanity.com/paper/2507.18551/) (2025)
- [ConKeD++ -- Improving descriptor learning for retinal image registration: A comprehensive study of contrastive losses](https://mlsanity.com/paper/2404.16773/) (2024)
- [Auto-regressive transformation for image alignment](https://mlsanity.com/paper/2505.04864/) (2025)
- [Gaussian Primitive Optimized Deformable Retinal Image Registration](https://mlsanity.com/paper/2508.16852/) (2025)
- [Establishing Robust Retinal Eye Tracking: A Weakly Supervised Algorithmic Framework](https://mlsanity.com/paper/2605.09181/) (2026)
- [Utilizing Radiomic Feature Analysis For Automated MRI Keypoint Detection: Enhancing Graph Applications](https://mlsanity.com/paper/2311.18281/) (2023)
- [Unsupervised Deep Learning-based Keypoint Localization Estimating Descriptor Matching Performance](https://mlsanity.com/paper/2505.02779/) (2025)
- [Unsupervised training of keypoint-agnostic descriptors for flexible retinal image registration](https://mlsanity.com/paper/2505.02787/) (2025)
- [Cross-modal Fundus Image Registration under Large FoV Disparity](https://mlsanity.com/paper/2512.12657/) (2025)
- [Active Diffusion Matching: Score-based Iterative Alignment of Cross-Modal Retinal Images](https://mlsanity.com/paper/2604.10084/) (2026)
- [Particle Diffusion Matching: Random Walk Correspondence Search for the Alignment of Standard and Ultra-Widefield Fundus Images](https://mlsanity.com/paper/2604.10085/) (2026)
- [XFeat Revisited: Reproducibility and Evaluation of a Lightweight Image Matcher](https://mlsanity.com/paper/2608.09519/) (2026)
- [Modality-Invariant Coarse-to-Fine Retinal Image Registration](https://mlsanity.com/paper/2608.14829/) (2026)
- [CrossFeat: Bridging Imaging Modalities in Feature Descriptor Space](https://mlsanity.com/paper/2609.00272/) (2026)

## Abstract (arXiv)

For retinal image matching (RIM), we propose SuperRetina, the first end-to-end method with jointly trainable keypoint detector and descriptor. SuperRetina is trained in a novel semi-supervised manner. A small set of (nearly 100) images are incompletely labeled and used to supervise the network to detect keypoints on the vascular tree. To attack the incompleteness of manual labeling, we propose Progressive Keypoint Expansion to enrich the keypoint labels at each training epoch. By utilizing a keypoint-based improved triplet loss as its description loss, SuperRetina produces highly discriminative descriptors at full input image size. Extensive experiments on multiple real-world datasets justify the viability of SuperRetina. Even with manual labeling replaced by auto labeling and thus making the training process fully manual-annotation free, SuperRetina compares favorably against a number of strong baselines for two RIM tasks, i.e. image registration and identity verification. SuperRetina will be open source.