Cargando…

PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones

Over the past few years, different Computer-Aided Diagnosis (CAD) systems have been proposed to tackle skin lesion analysis. Most of these systems work only for dermoscopy images since there is a strong lack of public clinical images archive available to evaluate the aforementioned CAD systems. To f...

Descripción completa

Detalles Bibliográficos
Autores principales: Pacheco, Andre G.C., Lima, Gustavo R., Salomão, Amanda S., Krohling, Breno, Biral, Igor P., de Angelo, Gabriel G., Alves Jr, Fábio C.R., Esgario, José G.M., Simora, Alana C., Castro, Pedro B.C., Rodrigues, Felipe B., Frasson, Patricia H.L., Krohling, Renato A., Knidel, Helder, Santos, Maria C.S., do Espírito Santo, Rachel B., Macedo, Telma L.S.G., Canuto, Tania R.P., de Barros, Luíz F.S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7479321/
https://www.ncbi.nlm.nih.gov/pubmed/32939378
http://dx.doi.org/10.1016/j.dib.2020.106221
_version_ 1783580246537142272
author Pacheco, Andre G.C.
Lima, Gustavo R.
Salomão, Amanda S.
Krohling, Breno
Biral, Igor P.
de Angelo, Gabriel G.
Alves Jr, Fábio C.R.
Esgario, José G.M.
Simora, Alana C.
Castro, Pedro B.C.
Rodrigues, Felipe B.
Frasson, Patricia H.L.
Krohling, Renato A.
Knidel, Helder
Santos, Maria C.S.
do Espírito Santo, Rachel B.
Macedo, Telma L.S.G.
Canuto, Tania R.P.
de Barros, Luíz F.S.
author_facet Pacheco, Andre G.C.
Lima, Gustavo R.
Salomão, Amanda S.
Krohling, Breno
Biral, Igor P.
de Angelo, Gabriel G.
Alves Jr, Fábio C.R.
Esgario, José G.M.
Simora, Alana C.
Castro, Pedro B.C.
Rodrigues, Felipe B.
Frasson, Patricia H.L.
Krohling, Renato A.
Knidel, Helder
Santos, Maria C.S.
do Espírito Santo, Rachel B.
Macedo, Telma L.S.G.
Canuto, Tania R.P.
de Barros, Luíz F.S.
author_sort Pacheco, Andre G.C.
collection PubMed
description Over the past few years, different Computer-Aided Diagnosis (CAD) systems have been proposed to tackle skin lesion analysis. Most of these systems work only for dermoscopy images since there is a strong lack of public clinical images archive available to evaluate the aforementioned CAD systems. To fill this gap, we release a skin lesion benchmark composed of clinical images collected from smartphone devices and a set of patient clinical data containing up to 21 features. The dataset consists of 1373 patients, 1641 skin lesions, and 2298 images for six different diagnostics: three skin diseases and three skin cancers. In total, 58.4% of the skin lesions are biopsy-proven, including 100% of the skin cancers. By releasing this benchmark, we aim to support future research and the development of new tools to assist clinicians to detect skin cancer.
format Online
Article
Text
id pubmed-7479321
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-74793212020-09-15 PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones Pacheco, Andre G.C. Lima, Gustavo R. Salomão, Amanda S. Krohling, Breno Biral, Igor P. de Angelo, Gabriel G. Alves Jr, Fábio C.R. Esgario, José G.M. Simora, Alana C. Castro, Pedro B.C. Rodrigues, Felipe B. Frasson, Patricia H.L. Krohling, Renato A. Knidel, Helder Santos, Maria C.S. do Espírito Santo, Rachel B. Macedo, Telma L.S.G. Canuto, Tania R.P. de Barros, Luíz F.S. Data Brief Data Article Over the past few years, different Computer-Aided Diagnosis (CAD) systems have been proposed to tackle skin lesion analysis. Most of these systems work only for dermoscopy images since there is a strong lack of public clinical images archive available to evaluate the aforementioned CAD systems. To fill this gap, we release a skin lesion benchmark composed of clinical images collected from smartphone devices and a set of patient clinical data containing up to 21 features. The dataset consists of 1373 patients, 1641 skin lesions, and 2298 images for six different diagnostics: three skin diseases and three skin cancers. In total, 58.4% of the skin lesions are biopsy-proven, including 100% of the skin cancers. By releasing this benchmark, we aim to support future research and the development of new tools to assist clinicians to detect skin cancer. Elsevier 2020-08-25 /pmc/articles/PMC7479321/ /pubmed/32939378 http://dx.doi.org/10.1016/j.dib.2020.106221 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Pacheco, Andre G.C.
Lima, Gustavo R.
Salomão, Amanda S.
Krohling, Breno
Biral, Igor P.
de Angelo, Gabriel G.
Alves Jr, Fábio C.R.
Esgario, José G.M.
Simora, Alana C.
Castro, Pedro B.C.
Rodrigues, Felipe B.
Frasson, Patricia H.L.
Krohling, Renato A.
Knidel, Helder
Santos, Maria C.S.
do Espírito Santo, Rachel B.
Macedo, Telma L.S.G.
Canuto, Tania R.P.
de Barros, Luíz F.S.
PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones
title PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones
title_full PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones
title_fullStr PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones
title_full_unstemmed PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones
title_short PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones
title_sort pad-ufes-20: a skin lesion dataset composed of patient data and clinical images collected from smartphones
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7479321/
https://www.ncbi.nlm.nih.gov/pubmed/32939378
http://dx.doi.org/10.1016/j.dib.2020.106221
work_keys_str_mv AT pachecoandregc padufes20askinlesiondatasetcomposedofpatientdataandclinicalimagescollectedfromsmartphones
AT limagustavor padufes20askinlesiondatasetcomposedofpatientdataandclinicalimagescollectedfromsmartphones
AT salomaoamandas padufes20askinlesiondatasetcomposedofpatientdataandclinicalimagescollectedfromsmartphones
AT krohlingbreno padufes20askinlesiondatasetcomposedofpatientdataandclinicalimagescollectedfromsmartphones
AT biraligorp padufes20askinlesiondatasetcomposedofpatientdataandclinicalimagescollectedfromsmartphones
AT deangelogabrielg padufes20askinlesiondatasetcomposedofpatientdataandclinicalimagescollectedfromsmartphones
AT alvesjrfabiocr padufes20askinlesiondatasetcomposedofpatientdataandclinicalimagescollectedfromsmartphones
AT esgariojosegm padufes20askinlesiondatasetcomposedofpatientdataandclinicalimagescollectedfromsmartphones
AT simoraalanac padufes20askinlesiondatasetcomposedofpatientdataandclinicalimagescollectedfromsmartphones
AT castropedrobc padufes20askinlesiondatasetcomposedofpatientdataandclinicalimagescollectedfromsmartphones
AT rodriguesfelipeb padufes20askinlesiondatasetcomposedofpatientdataandclinicalimagescollectedfromsmartphones
AT frassonpatriciahl padufes20askinlesiondatasetcomposedofpatientdataandclinicalimagescollectedfromsmartphones
AT krohlingrenatoa padufes20askinlesiondatasetcomposedofpatientdataandclinicalimagescollectedfromsmartphones
AT knidelhelder padufes20askinlesiondatasetcomposedofpatientdataandclinicalimagescollectedfromsmartphones
AT santosmariacs padufes20askinlesiondatasetcomposedofpatientdataandclinicalimagescollectedfromsmartphones
AT doespiritosantorachelb padufes20askinlesiondatasetcomposedofpatientdataandclinicalimagescollectedfromsmartphones
AT macedotelmalsg padufes20askinlesiondatasetcomposedofpatientdataandclinicalimagescollectedfromsmartphones
AT canutotaniarp padufes20askinlesiondatasetcomposedofpatientdataandclinicalimagescollectedfromsmartphones
AT debarrosluizfs padufes20askinlesiondatasetcomposedofpatientdataandclinicalimagescollectedfromsmartphones