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...
Autores principales: | , , , , , , , , , , , , , , , , , , |
---|---|
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 |