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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: Jun 24, 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

Automatized self-supervised learning for skin lesion screening.

Vullnet Useini^1,2, Stephanie Tanadini-Lang^2,3, Quentin Lohmeyer¹

¹Department of Mechanical and Process Engineering, ETH Zurich, Leonhardstrasse 21, 8092, Zurich, Switzerland.

Scientific Reports

|June 3, 2024

Summary

This summary is machine-generated.

An AI tool aids dermatologists in detecting melanoma by identifying suspicious skin lesions. This artificial intelligence decision support system achieved 95% sensitivity, improving diagnostic confidence and agreement among experts.

Keywords:

Melanoma Screening Self-supervised learning Ugly duckling

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Area of Science:

Dermatology
Artificial Intelligence
Medical Imaging

Background:

Melanoma incidence is rising globally, presenting a significant challenge for early detection.
Total body screening (TBS) for melanoma requires specialized expertise in identifying suspicious pigmented lesions (ugly ducklings or UDs).
Current diagnostic methods can be time-consuming and dependent on clinician experience.

Purpose of the Study:

To develop and validate an AI decision support tool for identifying and characterizing UDs in total body images.
To assist healthcare professionals of all expertise levels in melanoma screening.
To improve the accuracy and efficiency of early melanoma detection.

Main Methods:

Utilized a state-of-the-art object detection algorithm to locate all skin lesions in wide-field patient images.
Employed a self-supervised AI approach to sort lesions by suspiciousness, contextualized to each patient.
Conducted a clinical validation study to assess the tool's performance against expert diagnoses.

Main Results:

The AI tool demonstrated an average sensitivity of 95% for identifying the top-10 most suspicious UDs.
Dermatologists reported increased confidence in their diagnoses when using the AI tool.
The AI tool achieved 100% majority agreement among experts when used for assistance.

Conclusions:

The AI decision support tool effectively identifies suspicious skin lesions, aiding in early melanoma detection.
This technology can help mitigate specialist shortages and reduce patient consultation times.
Further validation and dataset expansion are planned for future development.