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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.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Graphical abstract<\/jats:title>\n                \n              <\/jats:sec>","DOI":"10.1007\/s11517-024-03160-6","type":"journal-article","created":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T23:20:05Z","timestamp":1720221605000},"page":"3721-3736","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["ConKeD: multiview contrastive descriptor learning for keypoint-based retinal image registration"],"prefix":"10.1007","volume":"62","author":[{"given":"David","family":"Rivas-Villar","sequence":"first","affiliation":[]},{"given":"\u00c1lvaro S.","family":"Hervella","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9","family":"Rouco","sequence":"additional","affiliation":[]},{"given":"Jorge","family":"Novo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,6]]},"reference":[{"key":"3160_CR1","doi-asserted-by":"publisher","unstructured":"Viergever MA, Maintz JBA, Klein S, Murphy K, Staring M, Pluim JPW (2016) A survey of medical image registration-under review. 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