  Self-supervised contrastive learning has emerged as one of the most
successful deep learning paradigms. In this regard, it has seen extensive use
in image registration and, more recently, in the particular field of medical
image registration. In this work, we propose to test and extend and improve a
state-of-the-art framework for color fundus image registration, ConKeD. Using
the ConKeD framework we test multiple loss functions, adapting them to the
framework and the application domain. Furthermore, we evaluate our models using
the standarized benchmark dataset FIRE as well as several datasets that have
never been used before for color fundus registration, for which we are
releasing the pairing data as well as a standardized evaluation approach. Our
work demonstrates state-of-the-art performance across all datasets and metrics
demonstrating several advantages over current SOTA color fundus registration
methods
