Unsupervised training of keypoint-agnostic descriptors for flexible retinal image registration

ArXi:2505.02787v2 Announce Type: replace Current color fundus image registration approaches are limited, among other things, by the lack of labeled data, which is even significant in the medical domain, motivating the use of unsupervised learning. Therefore, in this work, we develop a novel unsupervised descriptor learning method that does not rely on keypoint detection. This enables the resulting descriptor network to be agnostic to the keypoint detector used during the registration inference.