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		<citationkey>AguiarCalOliSanDua:2021:CoImRe</citationkey>
		<title>PathoSpotter-Search: A Content-Based Image Retrieval Tool for Nephropathology</title>
		<format>On-line</format>
		<year>2021</year>
		<numberoffiles>1</numberoffiles>
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		<author>Aguiar, Ellen,</author>
		<author>Calumby, Rodrigo,</author>
		<author>Oliveira, Luciano,</author>
		<author>Santos, Washington,</author>
		<author>Duarte, Angelo,</author>
		<affiliation>Universidade Estadual de Feira de Santana</affiliation>
		<affiliation>Universidade Estadual de Feira de Santana</affiliation>
		<affiliation>Universidade Federal da Bahia</affiliation>
		<affiliation>Fundação Oswaldo Cruz</affiliation>
		<affiliation>Universidade Estadual de Feira de Santana</affiliation>
		<editor>Paiva, Afonso,</editor>
		<editor>Menotti, David,</editor>
		<editor>Baranoski, Gladimir V. G.,</editor>
		<editor>Proença, Hugo Pedro,</editor>
		<editor>Junior, Antonio Lopes Apolinario,</editor>
		<editor>Papa, João Paulo,</editor>
		<editor>Pagliosa, Paulo,</editor>
		<editor>dos Santos, Thiago Oliveira,</editor>
		<editor>e Sá, Asla Medeiros,</editor>
		<editor>da Silveira, Thiago Lopes Trugillo,</editor>
		<editor>Brazil, Emilio Vital,</editor>
		<editor>Ponti, Moacir A.,</editor>
		<editor>Fernandes, Leandro A. F.,</editor>
		<editor>Avila, Sandra,</editor>
		<e-mailaddress>angeloduarte@uefs.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 34 (SIBGRAPI)</conferencename>
		<conferencelocation>Gramado, RS, Brazil (virtual)</conferencelocation>
		<date>18-22 Oct. 2021</date>
		<publisher>Sociedade Brasileira de Computação</publisher>
		<publisheraddress>Porto Alegre</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Work in Progress</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>Computational Pathology, Content Based Image Retrieval, Nephropathology, Convolutional Networks.</keywords>
		<abstract>Nephropathologists typically organizes their repository of digital images of kidney biopsies in such a way that it is dif&#64257;cult to retrieve cases that have images similar to a picture under analysis. Having this in mind, we initiated the development of PathoSpotter-Search, a Content-Based Image Retrieval system for images of kidney biopsies. The system operates as a cloud service to avoid the need to install any software on the pathologists computer. Our approach combines a feature extractor followed by a similarity score calculator. We evaluated convolutional network (CN) architectures (VGG-16 (original and &#64257;ne-tuned) and Inception-ResNet, and a network used in the proprietary classi&#64257;er for glomerular hypercellularity), combined with Cosine and Euclidean distances as similarity scores. The &#64257;rst results have shown that the CN of the VGG16 combined with cosine distance yielded the best performance (precision &#8776; 53%). To assess the usability and functionality of the PathoSpotter-Search as a cloud service, the system was tested by nephropathologists and proved to be useful as a tool for retrieving similar images from their local repositories. Currently, we are working to improve the system precision to at least 70%, and evaluating strategies to retrieve similar images based on segments or tiles of the query image.</abstract>
		<language>en</language>
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