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dc.contributor.authorSánchez-Peralta, Luisa F.
dc.contributor.authorPagador, J. Blas
dc.contributor.authorPicón, Artzai
dc.contributor.authorCalderón, Ángel José
dc.contributor.authorPolo, Francisco
dc.contributor.authorAndraka, Nagore
dc.contributor.authorBilbao, Roberto
dc.contributor.authorGlover, Ben
dc.contributor.authorSaratxaga, Cristina L.
dc.contributor.authorSánchez-Margallo, Francisco M.
dc.date.accessioned2020-12-09T10:01:43Z
dc.date.available2020-12-09T10:01:43Z
dc.date.issued2020-11-28
dc.identifier.citationBibTeX RIS APA Harvard IEEE MLA Vancouver Chicago Sánchez-Peralta, Luisa F., J. Blas Pagador, Artzai Picón, Ángel José Calderón, Francisco Polo, Nagore Andraka, Roberto Bilbao, Ben Glover, Cristina L. Saratxaga, and Francisco M. Sánchez-Margallo. “PICCOLO White-Light and Narrow-Band Imaging Colonoscopic Dataset: A Performance Comparative of Models and Datasets.” Applied Sciences 10, no. 23 (November 28, 2020): 8501. doi:10.3390/app10238501.en
dc.identifier.urihttp://hdl.handle.net/11556/1029
dc.description.abstractColorectal cancer is one of the world leading death causes. Fortunately, an early diagnosis allows for e_ective treatment, increasing the survival rate. Deep learning techniques have shown their utility for increasing the adenoma detection rate at colonoscopy, but a dataset is usually required so the model can automatically learn features that characterize the polyps. In this work, we present the PICCOLO dataset, that comprises 3433 manually annotated images (2131 white-light images 1302 narrow-band images), originated from 76 lesions from 40 patients, which are distributed into training (2203), validation (897) and test (333) sets assuring patient independence between sets. Furthermore, clinical metadata are also provided for each lesion. Four di_erent models, obtained by combining two backbones and two encoder–decoder architectures, are trained with the PICCOLO dataset and other two publicly available datasets for comparison. Results are provided for the test set of each dataset. Models trained with the PICCOLO dataset have a better generalization capacity, as they perform more uniformly along test sets of all datasets, rather than obtaining the best results for its own test set. This dataset is available at the website of the Basque Biobank, so it is expected that it will contribute to the further development of deep learning methods for polyp detection, localisation and classification, which would eventually result in a better and earlier diagnosis of colorectal cancer, hence improving patient outcomes.en
dc.description.sponsorshipThis work was partially supported by PICCOLO project. This project has received funding from the European Union’s Horizon2020 research and innovation programme under grant agreement No 732111. Furthermore, this publication has also been partially supported by GR18199 from Consejería de Economía, Ciencia y Agenda Digital of Junta de Extremadura (co-funded by European Regional Development Fund–ERDF. “A way to make Europe”/ “Investing in your future”. This work has been performed by the ICTS “NANBIOSIS” at the Jesús Usón Minimally Invasive Surgery Centre.en
dc.language.isoengen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titlePICCOLO White-Light and Narrow-Band Imaging Colonoscopic Dataset: A Performance Comparative of Models and Datasetsen
dc.typearticleen
dc.identifier.doi10.3390/app10238501en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/732111/EU/Multimodal highly-sensitive PhotonICs endoscope for improved in-vivo COLOn Cancer diagnosis and clinical decision support/PICCOLOen
dc.rights.accessRightsopenAccessen
dc.subject.keywordsDeep learningen
dc.subject.keywordsColorectal canceren
dc.subject.keywordsPublic dataseten
dc.subject.keywordsClinical metadataen
dc.subject.keywordsColonoscopyen
dc.subject.keywordsBinary masksen
dc.subject.keywordsPolypsen
dc.subject.keywordsDetectionen
dc.subject.keywordsLocalizationen
dc.subject.keywordsSegmentationen
dc.identifier.essn2076-3417en
dc.issue.number23en
dc.journal.titleApplied Sciencesen
dc.page.initial8501en
dc.volume.number10en


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