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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 6, 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

Connecting Adaptive Perceptual Learning and Signal Detection Theory in Skin Cancer Screening.

Philip J Kellman^1,2, Sally Krasne³, Christine M Massey¹

¹Department of Psychology, University of California, Los Angeles, Los Angeles, CA 90095, USA.

Cogsci ... Annual Conference of the Cognitive Science Society. Cognitive Science Society (U.S.). Conference

|January 4, 2024

Summary

Signal detection theory (SDT) enhances adaptive learning for skin cancer screening. Incorporating SDT methods improved learning efficiency and fluency compared to standard accuracy-based methods.

Keywords:

adaptive learning cancer image interpretation dermatology medical image perception perceptual learning signal detection skin cancer

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Last Updated: Jul 6, 2025

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Published on: April 24, 2017

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

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Published on: August 18, 2022

Area of Science:

Cognitive Psychology
Medical Education
Machine Learning

Background:

Adaptive learning systems often rely solely on accuracy data, neglecting response bias.
This limitation is particularly problematic in complex perceptual classification tasks, such as medical diagnosis.
Signal detection theory (SDT) offers a framework to disentangle sensitivity from criterion, potentially improving adaptive learning.

Purpose of the Study:

To investigate if incorporating SDT methods enhances adaptive perceptual learning for skin cancer screening.
To compare the effectiveness of SDT-based adaptive sequencing and mastery criteria against standard methods.

Main Methods:

Undergraduate participants used a Skin Cancer Perceptual Adaptive Learning Module (PALM) to classify skin lesions.
Four adaptive conditions varied sequencing (standard vs. SDT) and retirement criteria (standard vs. SDT).
A control group received didactic video instruction.

Main Results:

All adaptive conditions significantly outperformed the non-adaptive control in learning efficiency and fluency.
SDT retirement criteria led to greater learning efficiency than standard accuracy-based criteria at both immediate and delayed posttests.
No significant difference was found between SDT and standard adaptive sequencing.

Conclusions:

SDT-based enhancements can significantly improve the efficiency of adaptive perceptual learning systems.
The findings suggest that incorporating SDT principles, particularly for mastery criteria, offers a promising avenue for optimizing medical education and expertise development.