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Related Concept Videos

Skin Cancer01:30

Skin Cancer

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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.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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Related Experiment Video

Updated: May 13, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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HiTrace: Hierarchical Class Tracing Approach for Open-Set Recognition on Skin Lesions.

Benny Wei-Yun Hsu, Vincent S Tseng

    IEEE Journal of Biomedical and Health Informatics
    |April 14, 2025
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    Summary

    This study introduces HiTrace, a novel hierarchical approach for open-set recognition in AI-powered skin lesion classification. It improves accuracy in identifying new skin conditions, enhancing medical diagnostics.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Medical Diagnostics

    Background:

    • Open-set Recognition (OSR) is crucial for reliable AI in medical diagnostics.
    • Existing OSR methods face challenges in identifying novel classes, especially in complex medical data.
    • Accurate classification of skin lesions is vital for timely patient care.

    Purpose of the Study:

    • To introduce HiTrace, a novel hierarchical class tracing approach for advancing OSR in skin lesion classification.
    • To address limitations in current OSR methods by simplifying the identification of new skin conditions.
    • To develop robust evaluation metrics for comprehensive OSR performance assessment.

    Main Methods:

    • Developed HiTrace, incorporating Hierarchy-Aware Prototype (HAP) learning, Distribution Enhancement (DE) module, and Potential Class Tracing Algorithm (PCTA).
    • Utilized a hierarchical taxonomy to streamline the classification of new skin conditions.
    • Introduced two novel metrics: Hierarchical Open-set Classification Score (HOC-Score) and Major-Type Accuracy for Open-set samples (MTACC-O).

    Main Results:

    • HiTrace demonstrated significant improvements on PAD-UFES-20 and ISIC 2019 datasets.
    • Achieved relative improvements of 15.3% and 21.1% in HOC-Score, and 12.3% and 5.8% in MTACC-O.
    • Maintained competitive closed-set performance while enhancing open-set classification capabilities.

    Conclusions:

    • HiTrace represents a significant advancement in OSR for medical diagnostics, particularly for skin lesion classification.
    • The study provides a novel three-stage analysis framework for OSR in a practical medical context.
    • This approach enhances early detection and treatment of skin diseases, impacting patient care and future AI research.