Close
Metadata

1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3RNK3C2
Repositorysid.inpe.br/sibgrapi/2018/08.30.16.03
Last Update2018:08.30.16.03.01 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2018/08.30.16.03.01
Metadata Last Update2022:06.14.00.09.12 (UTC) administrator
DOI10.1109/SIBGRAPI.2018.00031
Citation KeySantosPont:2018:RoFeSp
TitleRobust feature spaces from pre-trained deep network layers for skin lesion classification
FormatOn-line
Year2018
Access Date2024, May 18
Number of Files1
Size3433 KiB
2. Context
Author1 Santos, Fernando Pereira dos
2 Ponti, Moacir Antonelli
Affiliation1 Institute of Mathematical and Computer Sciences (ICMC) - University of São Paulo (USP)
2 Institute of Mathematical and Computer Sciences (ICMC) - University of São Paulo (USP)
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
e-Mail Addressfernando_persan@usp.br
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Date29 Oct.-1 Nov. 2018
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2018-08-30 16:03:01 :: fernando_persan@usp.br -> administrator ::
2022-06-14 00:09:12 :: administrator -> :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsdeep learning
convolutional neural networks
skin lesion classification
AbstractThe incidence of skin cancer in the world population is a public health concern, and the first diagnosis takes into account the appearance of lesions on skin. In this context, automated methods to aid the screening for malign lesions can be an important tool. However, the efficiency of developed methods depends directly on the quality of the generated feature space which may vary when considering different image datasets and sources. We present a detailed study of feature spaces obtained from deep convolutional networks (CNNs), using the benchmark PH2 dataset, considering three CNN architectures, as well as investigating different layers, impact of dimensionality reduction, use of colour quantisation and noise addition. Our results show that, features have discriminative capability comparable to competing methods with balanced accuracy 94%, and 95% with noise injection. Additionally, we present a study of fine-tuning and generalisation across image quantisation and noise levels, contributing to the discussion of learning features from deep networks and offering a guideline for future works.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2018 > Robust feature spaces...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Robust feature spaces...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 30/08/2018 13:03 1.2 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3RNK3C2
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3RNK3C2
Languageen
Target Filepaper_id_28.pdf
User Groupfernando_persan@usp.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3RPADUS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2018/09.03.20.37 13
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination 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 secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


Close