Quality Assessment of Digital Colposcopies
Donated on 3/7/2017
This dataset explores the subjective quality assessment of digital colposcopies.
Dataset Characteristics
Multivariate
Subject Area
Health and Medicine
Associated Tasks
Classification
Feature Type
Real
# Instances
287
# Features
-
Dataset Information
Additional Information
* The dataset was acquired and annotated by professional physicians at 'Hospital Universitario de Caracas'. * The subjective judgments (target variables) were originally done in an ordinal manner (poor, fair, good, excellent) and was discretized in two classes (bad, good). * Images were randomly sampled from the original colposcopic sequences (videos). * The original images and the manual segmentations are included in the 'images' directory. * The dataset has three modalities (i.e. Hinselmann, Green, Schiller). * The target variables are expert::X (X in 0,...,5) and consensus.
Has Missing Values?
No
Variables Table
| Variable Name | Role | Type | Demographic | Description | Units | Missing Values |
|---|---|---|---|---|---|---|
| no | ||||||
| no | ||||||
| no | ||||||
| no | ||||||
| no | ||||||
| no | ||||||
| no | ||||||
| no | ||||||
| no | ||||||
| no |
0 to 10 of 69
Additional Variable Information
Three modalities: hinselmann, green, schiller. Number of Attributes: 69 (62 predictive attributes, 7 target variables) cervix_area: image area with cervix. os_area: image area with external os. walls_area: image area with vaginal walls. speculum_area: image area with the speculum. artifacts_area: image area with artifacts. cervix_artifacts_area: cervix area with the artifacts. os_artifacts_area: external os area with the artifacts. walls_artifacts_area: vaginal walls with the artifacts. speculum_artifacts_area: speculum area with the artifacts. cervix_specularities_area: cervix area with the specular reflections. os_specularities_area: external os area with the specular reflections. walls_specularities_area: vaginal walls area with the specular reflections. speculum_specularities_area: speculum area with the specular reflections. specularities_area: total area with specular reflections. area_h_max_diff: maximum area differences between the four cervix quadrants. rgb_cervix_r_mean: average color information in the cervix (R channel). rgb_cervix_r_std: stddev color information in the cervix (R channel). rgb_cervix_r_mean_minus_std: (avg - stddev) color information in the cervix (R channel). rgb_cervix_r_mean_plus_std: (avg + stddev) information in the cervix (R channel). rgb_cervix_g_mean: average color information in the cervix (G channel). rgb_cervix_g_std: stddev color information in the cervix (G channel). rgb_cervix_g_mean_minus_std: (avg - stddev) color information in the cervix (G channel). rgb_cervix_g_mean_plus_std: (avg + stddev) color information in the cervix (G channel). rgb_cervix_b_mean: average color information in the cervix (B channel). rgb_cervix_b_std: stddev color information in the cervix (B channel). rgb_cervix_b_mean_minus_std: (avg - stddev) color information in the cervix (B channel). rgb_cervix_b_mean_plus_std: (avg + stddev) color information in the cervix (B channel). rgb_total_r_mean: average color information in the image (B channel). rgb_total_r_std: stddev color information in the image (R channel). rgb_total_r_mean_minus_std: (avg - stddev) color information in the image (R channel). rgb_total_r_mean_plus_std: (avg + stddev) color information in the image (R channel). rgb_total_g_mean: average color information in the image (G channel). rgb_total_g_std: stddev color information in the image (G channel). rgb_total_g_mean_minus_std: (avg - stddev) color information in the image (G channel). rgb_total_g_mean_plus_std: (avg + stddev) color information in the image (G channel). rgb_total_b_mean: average color information in the image (B channel). rgb_total_b_std: stddev color information in the image (B channel). rgb_total_b_mean_minus_std: (avg - stddev) color information in the image (B channel). rgb_total_b_mean_plus_std: (avg + stddev) color information in the image (B channel). hsv_cervix_h_mean: average color information in the cervix (H channel). hsv_cervix_h_std: stddev color information in the cervix (H channel). hsv_cervix_s_mean: average color information in the cervix (S channel). hsv_cervix_s_std: stddev color information in the cervix (S channel). hsv_cervix_v_mean: average color information in the cervix (V channel). hsv_cervix_v_std: stddev color information in the cervix (V channel). hsv_total_h_mean: average color information in the image (H channel). hsv_total_h_std: stddev color information in the image (H channel). hsv_total_s_mean: average color information in the image (S channel). hsv_total_s_std: stddev color information in the image (S channel). hsv_total_v_mean: average color information in the image (V channel). hsv_total_v_std: stddev color information in the image (V channel). fit_cervix_hull_rate: Coverage of the cervix convex hull by the cervix. fit_cervix_hull_total: Image coverage of the cervix convex hull. fit_cervix_bbox_rate: Coverage of the cervix bounding box by the cervix. fit_cervix_bbox_total: Image coverage of the cervix bounding box. fit_circle_rate: Coverage of the cervix circle by the cervix. fit_circle_total: Image coverage of the cervix circle. fit_ellipse_rate: Coverage of the cervix ellipse by the cervix. fit_ellipse_total: Image coverage of the cervix ellipse. fit_ellipse_goodness: Goodness of the ellipse fitting. dist_to_center_cervix: Distance between the cervix center and the image center. dist_to_center_os: Distance between the cervical os center and the image center. experts::0: subjective assessment of the Expert 0 (target variable). experts::1: subjective assessment of the Expert 1 (target variable). experts::2: subjective assessment of the Expert 2 (target variable). experts::3: subjective assessment of the Expert 3 (target variable). experts::4: subjective assessment of the Expert 4 (target variable). experts::5: subjective assessment of the Expert 5 (target variable). consensus: subjective assessment of the consensus (target variable).
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset quality_assessment_of_digital_colposcopies = fetch_ucirepo(id=384) # data (as pandas dataframes) X = quality_assessment_of_digital_colposcopies.data.features y = quality_assessment_of_digital_colposcopies.data.targets # metadata print(quality_assessment_of_digital_colposcopies.metadata) # variable information print(quality_assessment_of_digital_colposcopies.variables)
Fernandes,Kelwin, Cardoso,Jaime, and Fernandes,Jessica. (2017). Quality Assessment of Digital Colposcopies. UCI Machine Learning Repository. https://doi.org/10.24432/C5C022.
@misc{misc_quality_assessment_of_digital_colposcopies_384,
author = {Fernandes,Kelwin, Cardoso,Jaime, and Fernandes,Jessica},
title = {{Quality Assessment of Digital Colposcopies}},
year = {2017},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5C022}
}Creators
Kelwin Fernandes
Jaime Cardoso
Jessica Fernandes
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.