@InProceedings{SaitoRezeFalc:2015:AcLeIn,
               author = "Saito, Priscila T. M. and de Rezende, Pedro J. and Falc{\~a}o, 
                         Alexandre Xavier",
          affiliation = "{Federal University of Technology - Parana} and {University of 
                         Campinas} and {University of Campinas}",
                title = "Active Learning with Interactive Response Time and its Application 
                         to the Diagnosis of Parasites",
            booktitle = "Proceedings...",
                 year = "2015",
               editor = "Segundo, Maur{\'{\i}}cio Pamplona and Faria, Fabio Augusto",
         organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "active learning, pattern recognition, automated diagnosis of 
                         intestinal parasites, microscopy image analysis, optimum-path 
                         forest classifiers.",
             abstract = "We have developed an automated system for the diagnosis of 
                         intestinal parasites from optical microscopy images. Each exam 
                         produces about 2,000 images with hundreds of objects in each image 
                         for classification as one out of the 15 most common species of 
                         parasites or impurity. As the number of exams increases, a dataset 
                         with unlabeled samples for classification grows in size. 
                         Impurities are numerous and diverse, with similar features to 
                         several species of parasites. Some species are also difficult to 
                         be differentiated. In this context, datasets are large and 
                         unbalanced, making the identification of the best samples for 
                         expert supervision crucial for the design of an effective 
                         classifier. We have addressed the problem by proposing a new 
                         paradigm for active learning, in which the dataset can be a priori 
                         reduced and/or organized to make that process realistic 
                         (efficient) for user interaction and yet more effective. We have 
                         also proposed several active learning methods under this paradigm 
                         and evaluated them for the diagnosis of intestinal parasites and 
                         other applications. Data reduction and/or organization avoid to 
                         reprocess the large dataset at each learning iteration, enabling 
                         to halt sample selection after a desired number of samples per 
                         iteration, which yields interactive response times. The proposed 
                         methods were validated in comparison with state-of-the-art 
                         approaches. Experiments included three datasets with parasites 
                         and/or impurities. One with 1,944 parasites (without impurities) 
                         and another with almost 6,000 labeled objects were used to develop 
                         the methods. A more realistic one, with over 140,000 unlabeled 
                         objects, unbalanced classes, absence of classes, and considerably 
                         higher number of impurities, was used for final validation by an 
                         expert in Parasitology.",
  conference-location = "Salvador, BA, Brazil",
      conference-year = "26-29 Aug. 2015",
             language = "en",
                  ibi = "8JMKD3MGPBW34M/3JUHF8B",
                  url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3JUHF8B",
           targetfile = "2015-wtd-sibgrapi-camera-ready-submitted.pdf",
        urlaccessdate = "2024, May 05"
}