@InProceedings{AguiarCalOliSanDua:2021:CoImRe,
               author = "Aguiar, Ellen and Calumby, Rodrigo and Oliveira, Luciano and 
                         Santos, Washington and Duarte, Angelo",
          affiliation = "{Universidade Estadual de Feira de Santana} and {Universidade 
                         Estadual de Feira de Santana} and {Universidade Federal da Bahia} 
                         and {Funda{\c{c}}{\~a}o Oswaldo Cruz} and {Universidade Estadual 
                         de Feira de Santana}",
                title = "PathoSpotter-Search: A Content-Based Image Retrieval Tool for 
                         Nephropathology",
            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 = "Computational Pathology, Content Based Image Retrieval, 
                         Nephropathology, Convolutional Networks.",
             abstract = "Nephropathologists typically organizes their repository of digital 
                         images of kidney biopsies in such a way that it is 
                         dif\ficult 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 \fine-tuned) and Inception-ResNet, and a 
                         network used in the proprietary classi\fier for glomerular 
                         hypercellularity), combined with Cosine and Euclidean distances as 
                         similarity scores. The \first results have shown that the 
                         CN of the VGG16 combined with cosine distance yielded the best 
                         performance (precision \≈ 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.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45EJ2DS",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45EJ2DS",
           targetfile = "PathoSpotter-Search_ A Content-Based Image Retrieval Tool for 
                         Nephropathology.pdf",
        urlaccessdate = "2024, Apr. 27"
}