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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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A Collaborative Learning Model for Skin Lesion Segmentation and Classification.

Ying Wang^1,2, Jie Su^1,2, Qiuyu Xu^1,2

¹School of Information Science and Engineering, University of Jinan, Jinan 250022, China.

Diagnostics (Basel, Switzerland)

|March 11, 2023

Summary

This summary is machine-generated.

This study introduces a collaborative learning model for skin lesion segmentation and classification, improving accuracy in computer-aided diagnosis, especially with limited data. The method enhances both tasks by leveraging their correlation for better skin cancer detection.

Keywords:

class activation mapping classification segmentation self-training skin cancer

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Area of Science:

Dermatology
Computer Vision
Medical Imaging

Background:

Automatic segmentation and classification of skin lesions are crucial for computer-aided skin cancer diagnosis.
Existing methods often study segmentation and classification independently, potentially missing synergistic benefits.

Purpose of the Study:

To propose a collaborative learning deep convolutional neural networks (CL-DCNN) model for enhanced dermatological segmentation and classification.
To leverage the correlation between segmentation and classification tasks, particularly in scenarios with insufficient data.

Main Methods:

Implemented a teacher-student learning approach within the CL-DCNN framework.
Utilized a self-training method with a reliability measure for generating high-quality pseudo-labels.
Employed class activation maps for improved segmentation localization and segmentation masks for enhanced classification.

Main Results:

Achieved a Jaccard index of 79.1% for skin lesion segmentation.
Obtained an average AUC of 93.7% for skin disease classification.
Demonstrated superior performance compared to advanced methods on ISIC 2017 and ISIC Archive datasets.

Conclusions:

The CL-DCNN model effectively utilizes the correlation between segmentation and classification tasks.
The proposed collaborative approach significantly improves the accuracy of both skin lesion segmentation and classification.
This method offers a promising direction for advancing computer-aided skin cancer diagnosis, especially with limited datasets.