ConKeD: Multiview contrastive descriptor learning for keypoint-based
  retinal image registration
David Rivas-Villar Álvaro S. Hervella José Rouco Jorge Novo
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.