PICCOLO White-Light and Narrow-Band Imaging Colonoscopic Dataset: A Performance Comparative of Models and Datasets
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2020-11-28
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Multidisciplinary Digital Publishing Institute (MDPI)
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Abstract
Colorectal 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.
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BibTeX 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.