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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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A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification.

Yutong Xie, Jianpeng Zhang, Yong Xia

IEEE Transactions on Medical Imaging

|February 20, 2020

Summary

This study introduces a novel deep learning model for simultaneous skin cancer segmentation and classification. The mutual bootstrapping approach improves accuracy in both tasks, advancing computer-aided diagnosis.

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Area of Science:

Medical image analysis
Computer-aided diagnosis
Deep learning

Background:

Automated skin lesion segmentation and classification are critical for early skin cancer detection.
Current deep learning models often focus on either segmentation or classification, missing synergistic benefits.

Purpose of the Study:

To propose a novel deep learning model, Mutual Bootstrapping Deep Convolutional Neural Networks (MB-DCNN), for simultaneous skin lesion segmentation and classification.
To enhance the accuracy of both segmentation and classification tasks through mutual knowledge transfer.

Main Methods:

Developed a MB-DCNN model comprising coarse segmentation, mask-guided classification, and enhanced segmentation networks.
Implemented a novel rank loss combined with Dice loss to handle class and pixel imbalance.
Evaluated the model on ISIC-2017 and PH2 datasets.

Main Results:

Achieved a Jaccard index of 80.4% and 89.4% for skin lesion segmentation.
Obtained an average AUC of 93.8% and 97.7% for skin lesion classification.
Outperformed existing state-of-the-art methods in both segmentation and classification tasks.

Conclusions:

Simultaneous segmentation and classification using a unified MB-DCNN model significantly boosts performance.
Mutual knowledge transfer between networks is effective for improving computer-aided diagnosis of skin cancer.
The proposed model offers a promising approach for more accurate and efficient skin cancer detection.