@InProceedings{GalvãoReSaOlDuAn:2021:AvMoDe,
               author = "Galv{\~a}o, Abel Ramalho and Rehem, Jonathan Moreira Cardozo and 
                         Santos, Washington Lu{\'{\i}}s Conrado dos and Oliveira, Luciano 
                         Rebou{\c{c}}as de and Duarte, Angelo Am{\^a}ncio and Angelo, 
                         Michele F{\'u}lvia",
          affiliation = "{Universidade Estadual de Feira de Santana (UEFS)} and 
                         {Universidade Estadual de Feira de Santana (UEFS)} and {Centro de 
                         Pesquisas Gon{\c{c}}alo Muniz da Funda{\c{c}}{\~a}o Oswaldo 
                         Cruz (CpqGM/FIOCRUZ)} and {Universidade Federal da Bahia (UFBA)} 
                         and {Universidade Estadual de Feira de Santana (UEFS)} and 
                         {Universidade Estadual de Feira de Santana (UEFS)}",
                title = "Avalia{\c{c}}{\~a}o de Modelos de Detec{\c{c}}{\~a}o de 
                         Objetos para Detectar Glom{\'e}rulos em Imagens 
                         Histol{\'o}gicas",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Glom{\'e}rulos, Detec{\c{c}}{\~a}o autom{\'a}tica, Deep 
                         learning, PathoSpotter.",
             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.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "pt",
                  ibi = "8JMKD3MGPEW34M/45EK7EB",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45EK7EB",
           targetfile = "paper.pdf",
        urlaccessdate = "2024, May 04"
}