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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3JUHF8B
Repositorysid.inpe.br/sibgrapi/2015/07.31.16.36
Last Update2015:08.04.13.14.11 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2015/07.31.16.36.52
Metadata Last Update2022:06.18.19.36.02 (UTC) administrator
Citation KeySaitoRezeFalc:2015:AcLeIn
TitleActive Learning with Interactive Response Time and its Application to the Diagnosis of Parasites
FormatOn-line
Year2015
Access Date2024, May 05
Secondary TypeDoctoral Work
Number of Files1
Size1963 KiB
2. Context
Author1 Saito, Priscila T. M.
2 de Rezende, Pedro J.
3 Falcão, Alexandre Xavier
Affiliation1 Federal University of Technology - Parana
2 University of Campinas
3 University of Campinas
EditorSegundo, Maurício Pamplona
Faria, Fabio Augusto
e-Mail Addresspsaito@utfpr.edu.br
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador, BA, Brazil
Date26-29 Aug. 2015
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeMaster's or Doctoral Work
History (UTC)2015-07-31 16:36:52 :: psaito@utfpr.edu.br -> administrator ::
2015-08-04 00:49:54 :: administrator -> psaito@utfpr.edu.br :: 2015
2015-08-04 13:14:11 :: psaito@utfpr.edu.br -> administrator :: 2015
2022-06-18 19:36:02 :: administrator -> :: 2015
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsactive learning
pattern recognition
automated diagnosis of intestinal parasites
microscopy image analysis
optimum-path forest classifiers
AbstractWe 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.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2015 > Active Learning with...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3JUHF8B
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3JUHF8B
Languageen
Target File2015-wtd-sibgrapi-camera-ready-submitted.pdf
User Grouppsaito@utfpr.edu.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPBW34M/3K24PF8
Citing Item Listsid.inpe.br/sibgrapi/2015/08.03.22.49 11
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


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