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Skin Cancer01:30

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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
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Related Experiment Video

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Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients
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Bridging the Gap Between Vitiligo Segmentation and Clinical Scores.

Yanling Li, Steven Tien Guan Thng, Adams Wai-Kin Kong

    IEEE Journal of Biomedical and Health Informatics
    |December 15, 2023
    PubMed
    Summary

    This study introduces a new full-body image analysis method for vitiligo, improving objective assessment and treatment planning. The algorithm achieves higher accuracy than dermatologists, aiding in precise vitiligo evaluation.

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    Area of Science:

    • Dermatology
    • Medical Imaging
    • Computer Vision

    Background:

    • Quantitative vitiligo evaluation is essential for treatment monitoring but faces challenges with objectivity and workload.
    • Current automatic segmentation methods focus on patch-wise images, limiting clinical score calculation and full-body assessment.
    • Full-body vitiligo segmentation is needed for comprehensive patient monitoring and objective clinical scoring.

    Purpose of the Study:

    • To develop and validate a novel algorithm for full-body vitiligo segmentation.
    • To address challenges in segmenting vitiligo from full-body images, including varied lighting and lesion distribution.
    • To enable objective, clinically translatable scoring of vitiligo extent from full-body images.

    Main Methods:

    • Establishment of the first full-body vitiligo dataset (1740 images) adhering to international standards.
    • Development of a custom algorithm incorporating contrast enhancement and long-range comparison for segmentation.
    • Introduction of a confidence score refinement module to handle challenging cases like fully affected or unaffected skin areas.

    Main Results:

    • The proposed algorithm significantly reduced average per-image vitiligo involvement percentage error from 3.69% to 1.81%.
    • Top 10% per-image errors decreased substantially from 23.17% to 8.29%.
    • Per-patient vitiligo involvement percentage errors (mean 1.17%, max 3.11%) surpassed experienced dermatologist evaluations.

    Conclusions:

    • The developed full-body vitiligo segmentation algorithm provides objective and clinically applicable quantitative evaluations.
    • The method overcomes limitations of previous patch-wise approaches and handles complex imaging conditions.
    • This advancement offers a more accurate and efficient tool for assessing vitiligo progression and treatment response.