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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: May 24, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Class-Agnostic Feature-Learning-Based Deep-Learning Model for Robust Melanoma Prediction.

Yuseong Chu, Solam Lee, Byungho Oh

IEEE Journal of Biomedical and Health Informatics

|March 3, 2025

Summary

This summary is machine-generated.

This study introduces a robust deep learning model using class-agnostic activation maps (CAAMs) to improve melanoma prediction accuracy despite image variations. The method enhances diagnostic reliability for skin lesion analysis.

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Area of Science:

Dermatology and Medical Imaging
Artificial Intelligence in Healthcare
Computer Vision

Background:

Skin lesion imaging variability, particularly in positioning, challenges deep learning model diagnostic accuracy.
Inconsistent model activations due to image transformations reduce the reliability of melanoma detection.

Purpose of the Study:

To develop a robust deep learning model for melanoma prediction that addresses image variability and transformation robustness.
To enhance diagnostic accuracy and reliability in skin lesion analysis using class-agnostic activation maps (CAAMs).

Main Methods:

Utilized the International Skin Imaging Collaboration (ISIC) 2017 and 2019 datasets, focusing on melanoma and nevus classification.
Developed a deep learning model incorporating class-agnostic activation maps (CAAMs) to ensure robust feature learning.
Evaluated model performance using Area Under the Receiver Operating Characteristic Curve (AUROC) and robustness using Dice scores.

Main Results:

Achieved an AUROC of 0.954 for ConvNeXt on the ISIC 2019 dataset, demonstrating high predictive performance.
Obtained Dice scores of 0.664 (ConvNeXt) and 0.457 (ResNet) on ISIC 2019, indicating improved robustness.
On the ISIC 2017 dataset, achieved an AUROC of 0.843 for ConvNeXt with Dice scores of 0.557 (ConvNeXt) and 0.306 (ResNet).

Conclusions:

The proposed CAAM-based deep learning method significantly improves melanoma prediction and lesion recognition accuracy.
The approach ensures robust and consistent activation maps, enhancing overall diagnostic reliability for skin lesions.
The developed method and code are publicly available for further research and application.