Retinal Images Registration via Unsupervised Deep Learning

  • Giovana Augusta Benvenuto UNESP
  • Wallace Casaca UNESP


In ophthalmology and vision science applications, aligning a pair of retinal images is of paramount importance to support disease diagnosis and routine eye examinations. This paper introduces an end-to-end framework capable of learning the registration task in a fully unsupervised manner. The proposed approach combines Convolutional Neural Networks and Spatial Transformer Network into a unified pipeline that incorporates a similarity metric to gauge the difference between the images, enabling image alignment without requiring any ground-truth data. The validation study demonstrates that the model can successfully deal with several categories of fundus images, surpassing other recent techniques for retinal registration.


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BENVENUTO, Giovana Augusta; CASACA, Wallace. Retinal Images Registration via Unsupervised Deep Learning. In: WORKSHOP DE TESES E DISSERTAÇÕES - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 36. , 2023, Rio Grande/RS. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 42-48. DOI: