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Skin Cancer

Skin Cancer

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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Updated: May 21, 2025

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

GatedSegDiff: a gated fusion diffusion model for skin lesion segmentation.

Rui Wang¹, Liucheng Yao¹, Jiawen Zeng¹

¹School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China.

Medical & Biological Engineering & Computing

|March 18, 2025

Summary

GatedSegDiff improves skin lesion segmentation accuracy, especially for fuzzy boundaries, aiding melanoma diagnosis. This deep learning model enhances diagnostic precision in digital dermatology.

Keywords:

Deep learning Diffusion models Gated fusion Melanoma diagnosis Skin lesion segmentation

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Area of Science:

Dermatology
Medical Image Analysis
Artificial Intelligence

Background:

Accurate skin lesion segmentation is critical for diagnosing skin diseases like melanoma.
Existing methods struggle with the fuzzy boundaries characteristic of many skin lesions.
Advanced image segmentation techniques are needed to improve diagnostic precision.

Purpose of the Study:

To develop an end-to-end framework, GatedSegDiff, for precise melanoma skin lesion segmentation.
To address the challenge of segmenting skin lesions with unclear boundaries.
To enhance the accuracy and efficiency of skin lesion analysis.

Main Methods:

Developed GatedSegDiff, an end-to-end framework integrating denoising networks and a gated attention fusion module.
Utilized semantic representation and multi-scale feature map merging for enhanced segmentation.
Evaluated the model on ISIC 2017, ISIC 2018, and PH2 skin lesion image datasets.

Main Results:

GatedSegDiff achieved an average 4.3% increase in IoU score across three datasets.
The model demonstrated a 1.5% decrease in HD95 score, indicating improved boundary accuracy.
Outperformed existing advanced methods in multiple performance metrics for skin lesion segmentation.

Conclusions:

GatedSegDiff significantly advances skin lesion segmentation, particularly for lesions with fuzzy boundaries.
The model enhances diagnostic precision and efficiency in digital dermatological applications.
Shows potential for clinical use in early skin disease diagnosis and treatment planning.