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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.
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Related Experiment Video

Updated: Jan 9, 2026

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
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Class-N-Diff: Classification-Induced Diffusion Model Can Make Fair Skin Cancer Diagnosis.

Nusrat Munia, Abdullah Imran

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary

    This study introduces Class-N-Diff, a novel generative model for synthetic medical imaging. It enhances the generation of realistic dermoscopic images for skin cancer diagnosis by integrating classification into the diffusion process.

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    Area of Science:

    • Artificial Intelligence
    • Medical Imaging
    • Computer Vision

    Background:

    • Generative models excel at synthetic data creation, particularly in medical imaging.
    • Traditional class-conditioned models face challenges in accurately generating specific medical image classes, impacting applications like skin cancer diagnosis.

    Purpose of the Study:

    • To develop a generative model capable of simultaneous image generation and classification for dermoscopic images.
    • To improve the accuracy and diversity of synthetic medical images for diagnostic tasks.

    Main Methods:

    • Proposed a classification-induced diffusion model named Class-N-Diff.
    • Integrated a convolutional classifier within the diffusion model to guide image generation based on class conditions.
    • Incorporated a classification objective into the generative process.

    Main Results:

    • Class-N-Diff demonstrated improved control over class-conditioned image synthesis, producing more realistic and diverse dermoscopic images.
    • The integrated classifier achieved enhanced performance, proving its utility for downstream diagnostic tasks.
    • The model offers a robust solution for improving synthetic dermoscopic image quality and applicability.

    Conclusions:

    • Class-N-Diff effectively addresses limitations in generating class-specific medical images.
    • The dual capability of generation and classification enhances the utility of synthetic dermoscopic images for AI-driven diagnostics.
    • This approach represents a significant advancement in generative modeling for medical applications.