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Quality Assessment of Digital Colposcopies Data Set
Download: Data Folder, Data Set Description

Abstract: This dataset explores the subjective quality assessment of digital colposcopies.

Data Set Characteristics:  

Multivariate

Number of Instances:

287

Area:

Life

Attribute Characteristics:

Real

Number of Attributes:

69

Date Donated

2017-03-08

Associated Tasks:

Classification

Missing Values?

N/A

Number of Web Hits:

22911


Source:

Kelwin Fernandes (kafc _at_ inesctec _dot_ pt) - INESC TEC & FEUP, Porto, Portugal.
Jaime S. Cardoso - INESC TEC & FEUP, Porto, Portugal.
Jessica Fernandes - Universidad Central de Venezuela, Caracas, Venezuela.


Data Set 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.


Attribute 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).


Relevant Papers:

Fernandes, Kelwin, Jaime S. Cardoso, and Jessica Fernandes. 'Transfer Learning with Partial Observability Applied to Cervical Cancer Screening.' Iberian Conference on Pattern Recognition and Image Analysis. Springer International Publishing, 2017.



Citation Request:

Fernandes, Kelwin, Jaime S. Cardoso, and Jessica Fernandes. 'Transfer Learning with Partial Observability Applied to Cervical Cancer Screening.' Iberian Conference on Pattern Recognition and Image Analysis. Springer International Publishing, 2017.


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