A Collaborative Learning Model for Skin Lesion Segmentation and Classification
The automatic segmentation and classification of skin lesions are two essential tasks in computer-aided skin cancer diagnosis. Segmentation aims to detect the location and boundary of the skin lesion area, while classification is used to evaluate the type of skin lesion. The location and contour inf...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/5/912 |
| _version_ | 1797615524996186112 |
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| author | Ying Wang Jie Su Qiuyu Xu Yixin Zhong |
| author_facet | Ying Wang Jie Su Qiuyu Xu Yixin Zhong |
| author_sort | Ying Wang |
| collection | DOAJ |
| description | The automatic segmentation and classification of skin lesions are two essential tasks in computer-aided skin cancer diagnosis. Segmentation aims to detect the location and boundary of the skin lesion area, while classification is used to evaluate the type of skin lesion. The location and contour information of lesions provided by segmentation is essential for the classification of skin lesions, while the skin disease classification helps generate target localization maps to assist the segmentation task. Although the segmentation and classification are studied independently in most cases, we find meaningful information can be explored using the correlation of dermatological segmentation and classification tasks, especially when the sample data are insufficient. In this paper, we propose a collaborative learning deep convolutional neural networks (CL-DCNN) model based on the teacher–student learning method for dermatological segmentation and classification. To generate high-quality pseudo-labels, we provide a self-training method. The segmentation network is selectively retrained through classification network screening pseudo-labels. Specially, we obtain high-quality pseudo-labels for the segmentation network by providing a reliability measure method. We also employ class activation maps to improve the location ability of the segmentation network. Furthermore, we provide the lesion contour information by using the lesion segmentation masks to improve the recognition ability of the classification network. Experiments are carried on the ISIC 2017 and ISIC Archive datasets. The CL-DCNN model achieved a Jaccard of 79.1% on the skin lesion segmentation task and an average AUC of 93.7% on the skin disease classification task, which is superior to the advanced skin lesion segmentation methods and classification methods. |
| first_indexed | 2024-03-11T07:27:46Z |
| format | Article |
| id | doaj.art-941e1bd8da704661af3ad2f85b95abbd |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T07:27:46Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-941e1bd8da704661af3ad2f85b95abbd2023-11-17T07:29:52ZengMDPI AGDiagnostics2075-44182023-02-0113591210.3390/diagnostics13050912A Collaborative Learning Model for Skin Lesion Segmentation and ClassificationYing Wang0Jie Su1Qiuyu Xu2Yixin Zhong3School of Information Science and Engineering, University of Jinan, Jinan 250022, ChinaSchool of Information Science and Engineering, University of Jinan, Jinan 250022, ChinaSchool of Information Science and Engineering, University of Jinan, Jinan 250022, ChinaSchool of Information Science and Engineering, University of Jinan, Jinan 250022, ChinaThe automatic segmentation and classification of skin lesions are two essential tasks in computer-aided skin cancer diagnosis. Segmentation aims to detect the location and boundary of the skin lesion area, while classification is used to evaluate the type of skin lesion. The location and contour information of lesions provided by segmentation is essential for the classification of skin lesions, while the skin disease classification helps generate target localization maps to assist the segmentation task. Although the segmentation and classification are studied independently in most cases, we find meaningful information can be explored using the correlation of dermatological segmentation and classification tasks, especially when the sample data are insufficient. In this paper, we propose a collaborative learning deep convolutional neural networks (CL-DCNN) model based on the teacher–student learning method for dermatological segmentation and classification. To generate high-quality pseudo-labels, we provide a self-training method. The segmentation network is selectively retrained through classification network screening pseudo-labels. Specially, we obtain high-quality pseudo-labels for the segmentation network by providing a reliability measure method. We also employ class activation maps to improve the location ability of the segmentation network. Furthermore, we provide the lesion contour information by using the lesion segmentation masks to improve the recognition ability of the classification network. Experiments are carried on the ISIC 2017 and ISIC Archive datasets. The CL-DCNN model achieved a Jaccard of 79.1% on the skin lesion segmentation task and an average AUC of 93.7% on the skin disease classification task, which is superior to the advanced skin lesion segmentation methods and classification methods.https://www.mdpi.com/2075-4418/13/5/912skin cancersegmentationclassificationself-trainingclass activation mapping |
| spellingShingle | Ying Wang Jie Su Qiuyu Xu Yixin Zhong A Collaborative Learning Model for Skin Lesion Segmentation and Classification Diagnostics skin cancer segmentation classification self-training class activation mapping |
| title | A Collaborative Learning Model for Skin Lesion Segmentation and Classification |
| title_full | A Collaborative Learning Model for Skin Lesion Segmentation and Classification |
| title_fullStr | A Collaborative Learning Model for Skin Lesion Segmentation and Classification |
| title_full_unstemmed | A Collaborative Learning Model for Skin Lesion Segmentation and Classification |
| title_short | A Collaborative Learning Model for Skin Lesion Segmentation and Classification |
| title_sort | collaborative learning model for skin lesion segmentation and classification |
| topic | skin cancer segmentation classification self-training class activation mapping |
| url | https://www.mdpi.com/2075-4418/13/5/912 |
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