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		<citationkey>BenvenutoCasa:2023:ReImRe</citationkey>
		<title>Retinal images registration via unsupervised deep learning</title>
		<format>On-line</format>
		<year>2023</year>
		<numberoffiles>1</numberoffiles>
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		<author>Benvenuto, Giovana Augusta,</author>
		<author>Casaca, Wallace,</author>
		<affiliation>UNESP</affiliation>
		<affiliation>UNESP</affiliation>
		<editor>Clua, Esteban Walter Gonzalez,</editor>
		<editor>Körting, Thales Sehn,</editor>
		<editor>Paulovich, Fernando Vieira,</editor>
		<editor>Feris, Rogerio,</editor>
		<e-mailaddress>giovana.a.benvenuto@unesp.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 36 (SIBGRAPI)</conferencename>
		<conferencelocation>Rio Grande, RS</conferencelocation>
		<date>Nov. 06-09, 2023</date>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Master's or Doctoral Work</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>Image registration, image processing, deep learning, retina.</keywords>
		<abstract>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.</abstract>
		<language>en</language>
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