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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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Related Experiment Video

Updated: May 23, 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

Deep pixel-wise supervision for skin lesion classification.

Aleksandra Dzieniszewska¹, Piotr Garbat¹, Ryszard Piramidowicz¹

¹Institute of Microelectronics and Optoelectronics, Warsaw University of Technology, Koszykowa 75, Warsaw, 00662, Masovian, Poland.

Computers in Biology and Medicine

|May 21, 2025

Summary

This summary is machine-generated.

This study introduces deep pixel-wise supervision for improved skin lesion classification, enhancing early detection of skin diseases. The novel approach significantly boosts diagnostic accuracy, aiding in better patient outcomes.

Keywords:

Auxiliary supervision Deep supervision Pixel-wise supervision Skin lesion

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Area of Science:

Dermatology
Computer Vision
Medical Imaging

Background:

Automated systems for skin lesion diagnosis can improve early detection and survival rates.
Current methods often miss local patterns by focusing on global features.

Purpose of the Study:

To enhance skin lesion classification by incorporating local pattern recognition.
To improve the accuracy and sensitivity of automated diagnostic systems.

Main Methods:

Implemented two deep pixel-wise supervision approaches: constant map and segmentation mask.
Applied supervision to each pixel in the network's feature map for detailed guidance.
Combined deep and pixel-wise supervision to focus network attention on critical lesion areas.

Main Results:

Achieved 90.7% accuracy (ISIC 2017) and 90.5% accuracy (PH2) for binary classification.
Reached 88% accuracy for nine-class classification on combined ISIC 2019/2020 datasets.
Demonstrated enhanced accuracy and sensitivity in experiments.

Conclusions:

Deep pixel-wise supervision significantly improves skin lesion classification.
The proposed method outperforms existing state-of-the-art techniques.
This approach validates the effectiveness of detailed pixel-level guidance for accurate diagnosis.