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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: Jan 10, 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

Curvature-aware selective feature interaction network for skin lesion segmentation.

Shudi Zhang¹, Junchang Xin², Qi Shen¹

¹College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110016, China.

Neural Networks : the Official Journal of the International Neural Network Society

|November 24, 2025

Summary

This summary is machine-generated.

A new Curvature-Aware Selective Feature Interaction Network (CASFI-Net) improves skin lesion segmentation by better capturing feature interactions and shape characteristics. This method enhances accuracy in medical image analysis for dermatological diseases.

Keywords:

Curvature selective Feature interaction Skin lesion segmentation

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Area of Science:

Medical Image Analysis
Computer Vision
Dermatology

Background:

Dermatological diseases and the need for accurate skin lesion segmentation are increasing.
Deep learning methods face challenges in capturing feature interactions and exploiting lesion shape characteristics.

Purpose of the Study:

To propose a novel network, CASFI-Net, for robust and accurate skin lesion segmentation.
To address the semantic gap in encoder-decoder architectures and reduce redundant information.

Main Methods:

Introduced a Multi-Grain Feature Interaction (MGFI) module with attention mechanisms to integrate features across resolutions.
Developed a Curvature-Aware Selective Feature (CASF) module to evaluate feature map curvature and select informative channels.
Implemented a fast curvature selection mechanism to emphasize edge features and reduce redundancy.

Main Results:

CASFI-Net demonstrated superior performance in skin lesion segmentation across three datasets.
The proposed network effectively bridges the semantic gap and exploits lesion shape characteristics.
Achieved state-of-the-art results with low computational cost.

Conclusions:

CASFI-Net offers a significant advancement in skin lesion segmentation accuracy.
The network effectively addresses key challenges in medical image analysis for dermatology.
CASFI-Net provides a computationally efficient and accurate solution for segmenting skin lesions.