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阴道镜序列(视频)中随机抽取图像数据集

阴道镜序列(视频)中随机抽取图像数据集

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Life Classification

Kelwin Fernandes (kafc _at_ inesctec _dot_ pt) - INESC TECFEUP, Porto, Portugal.Jaime S. Cardoso - INESC TECFEUP, Porto,......

数据结构 ? 33.7M

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    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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