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		<citationkey>GalvãoReSaOlDuAn:2021:AvMoDe</citationkey>
		<title>Avaliação de Modelos de Detecção de Objetos para Detectar Glomérulos em Imagens Histológicas</title>
		<format>On-line</format>
		<year>2021</year>
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		<author>Galvão, Abel Ramalho,</author>
		<author>Rehem, Jonathan Moreira Cardozo,</author>
		<author>Santos, Washington Luís Conrado dos,</author>
		<author>Oliveira, Luciano Rebouças de,</author>
		<author>Duarte, Angelo Amâncio,</author>
		<author>Angelo, Michele Fúlvia,</author>
		<affiliation>Universidade Estadual de Feira de Santana (UEFS)</affiliation>
		<affiliation>Universidade Estadual de Feira de Santana (UEFS)</affiliation>
		<affiliation>Centro de  Pesquisas Gonçalo Muniz da Fundação Oswaldo Cruz (CpqGM/FIOCRUZ)</affiliation>
		<affiliation>Universidade Federal da Bahia (UFBA)</affiliation>
		<affiliation>Universidade Estadual de Feira de Santana (UEFS)</affiliation>
		<affiliation>Universidade Estadual de Feira de Santana (UEFS)</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>abel.ramalho18@gmail.com</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>Glomérulos, Detecção automática, Deep learning, PathoSpotter.</keywords>
		<abstract>Glomeruli are renal structures responsible for filtering blood and can be affected by lesions. Currently, computer systems to help identify these lesions have been developed, and thus, the detection of these glomeruli is of great importance. The objective of this work is to evaluate the performance of object detection models for the detection of glomeruli in digital histological images. Three models were evaluated: SM1 (SSD Mobilenet v1), FRR50 (Faster RCNN Resnet 50) and FRR101 (Faster RCNN Resnet 101), of which the FRR50 model obtained the best result, mAP=0.88.</abstract>
		<language>pt</language>
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		<usergroup>abel.ramalho18@gmail.com</usergroup>
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