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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: Jul 13, 2026

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

TG-Net: Using text prompts for improved skin lesion segmentation.

Xiangfu Meng¹, Chunlin Yu¹, Zhichao Zhang¹

¹School of Electronics and Information Engineering, Liaoning Technical University, Huludao, China.

Computers in Biology and Medicine

|July 4, 2024

Summary

This study introduces TG-Net, a novel framework for skin cancer segmentation that uses diagnostic text to improve accuracy. TG-Net enhances early skin cancer detection by effectively segmenting lesions in dermoscopy images.

Keywords:

Medical image analysis Res2Net Skin lesion segmentation Text attention

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Area of Science:

Medical image analysis
Computer vision
Dermatology

Background:

Accurate skin lesion segmentation is crucial for early skin cancer diagnosis.
Challenges include variations in lesion texture, size, shape, position, and obscure boundaries in dermoscopy images.

Purpose of the Study:

To propose TG-Net, a novel framework that leverages textual diagnostic information to guide the segmentation of dermoscopic images for improved skin cancer detection.

Main Methods:

TG-Net utilizes a dual-stream encoder-decoder architecture with Res2Net for image features and a text attention (TA) block for textual features.
A multi-level fusion (MLF) module integrates features for global guidance.
Multi-scale reverse attention (MSRA) modules refine segmentation using local and global features.

Main Results:

TG-Net demonstrated superior performance compared to state-of-the-art methods on ISIC 2017, HAM10000, and PH2 datasets.
The framework effectively integrates textual information for enhanced lesion segmentation.

Conclusions:

TG-Net offers a reliable and effective approach for segmenting skin lesions in dermoscopy images by incorporating textual diagnostic data.
This method holds promise for improving early skin cancer diagnosis and treatment.