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

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From Deep Learning Towards Finding Skin Lesion Biomarkers.

Xiaoxiao Li, Junyan Wu, Eric Z Chen

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 18, 2020

Summary

This summary is machine-generated.

Deep learning models can identify new skin lesion biomarkers from dermoscopy images. These AI-discovered features, including surrounding skin, aid dermatologists in early melanoma detection and diagnosis.

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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

Area of Science:

Dermatology
Artificial Intelligence
Biomedical Imaging

Background:

Melanoma incidence is rapidly increasing, making early detection crucial for patient survival.
Dermoscopy aids early melanoma detection, but accurate classification by dermatologists remains challenging.
Deep learning (DL) shows promise in computer-assisted diagnosis but often lacks interpretability regarding used features.

Purpose of the Study:

To ensure DL methods utilize clinically relevant features for skin lesion diagnosis, not artifacts.
To discover novel, clinically meaningful biomarkers for melanoma diagnosis beyond traditional criteria.
To develop a pipeline for identifying and validating DL-derived biomarkers for skin lesion classification.

Main Methods:

Development of a DL pipeline to analyze dermoscopy images for skin lesion classification.
Validation of DL-identified features and biomarkers through agreement with dermatologists.
Investigation of features beyond the primary lesion, including surrounding skin characteristics.

Main Results:

The proposed pipeline successfully identified biomarkers for differentiating skin lesions.
Discovered patterns and biomarkers showed agreement with dermatologists' clinical judgment.
Surrounding skin features were identified as novel, significant biomarkers for skin lesion diagnosis, previously unconsidered in traditional rules.

Conclusions:

DL-powered analysis can uncover clinically relevant biomarkers for skin lesion diagnosis.
Novel biomarkers, including surrounding skin features, can enhance diagnostic accuracy.
The discovered biomarkers offer valuable guidance for clinical decision-making in melanoma diagnosis.