ConKeD++ -- Improving descriptor learning for retinal image registration: A comprehensive study of contrastive losses

arXiv:2404.16773v1 Announce Type: new
Abstract: 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, …

abstract arxiv cs.cv deep learning image improving losses medical regard registration study type work