# ConKeD: Multiview contrastive descriptor learning for keypoint-based retinal image registration

Source: [ConKeD: Multiview contrastive descriptor learning for keypoint-based retinal image registration](<https://www.sota2.com/research/paper/68fa73fd1e209131cea5f0e9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:59:03.375Z

Authors: David Rivas-Villar, \\'Alvaro S. Hervella, Jos\\'e Rouco, Jorge Novo

Published: 2024-01-11T13:22:54.000Z

## Abstract

Retinal image registration is of utmost importance due to its wide applications in medical practice. In this context, we propose ConKeD, a novel deep learning approach to learn descriptors for retinal image registration. In contrast to current registration methods, our approach employs a novel multi-positive multi-negative contrastive learning strategy that enables the utilization of additional information from the available training samples. This makes it possible to learn high quality descriptors from limited training data. To train and evaluate ConKeD, we combine these descriptors with domain-specific keypoints, particularly blood vessel bifurcations and crossovers, that are detected using a deep neural network. Our experimental results demonstrate the benefits of the novel multi-positive multi-negative strategy, as it outperforms the widely used triplet loss technique (single-positive and single-negative) as well as the single-positive multi-negative alternative. Additionally, the combination of ConKeD with the domain-specific keypoints produces comparable results to the state-of-the-art methods for retinal image registration, while offering important advantages such as avoiding pre-processing, utilizing fewer training samples, and requiring fewer detected keypoints, among others. Therefore, ConKeD shows a promising potential towards facilitating the development and application of deep learning-based methods for retinal image registration.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Retinal Image Registration on FIRE (overall)](<https://www.sota2.com/research/sota/retinal-image-registration-on-fire-overall.md>): Retinal Image Registration; FIRE (overall); A Score: 74.9.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2401.05901>)

[PDF](<https://arxiv.org/pdf/2401.05901.pdf>)