Classification of Microcalcification Clusters Using Bilateral Features Based on Graph Convolutional Network
Breast cancer is one of the diseases with the highest incidence and mortality among women in the world, which has posed a serious threat to women’s health. The appearance of clustered calcifications is one of the important signs of breast cancer, and thus how to classify clustered calcifications com...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.871662/full |
| _version_ | 1811258299832598528 |
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| author | Yaqin Zhang Jiayue Han Binghui Chen Lin Chang Ting Song Guanxiong Cai |
| author_facet | Yaqin Zhang Jiayue Han Binghui Chen Lin Chang Ting Song Guanxiong Cai |
| author_sort | Yaqin Zhang |
| collection | DOAJ |
| description | Breast cancer is one of the diseases with the highest incidence and mortality among women in the world, which has posed a serious threat to women’s health. The appearance of clustered calcifications is one of the important signs of breast cancer, and thus how to classify clustered calcifications comes to be a key breakthrough in controlling breast cancer. In this study, the discriminant model based on image convolution is used to learn the image features related to the classification of clustered microcalcifications, and the graph convolutional network (GCN) based on topological graph is used to learn the spatial distribution characteristics of clustered microcalcifications. These two models are fused to obtain a complementary model of image information and spatial information. The results show that the performance of the fusion model proposed in this paper is obviously superior to that of the two classification models in the classification of clustered microcalcification. |
| first_indexed | 2024-04-12T18:11:04Z |
| format | Article |
| id | doaj.art-6112bf6e0bf5481d9dede75ed07476ed |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-12T18:11:04Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-6112bf6e0bf5481d9dede75ed07476ed2022-12-22T03:21:49ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-05-011210.3389/fonc.2022.871662871662Classification of Microcalcification Clusters Using Bilateral Features Based on Graph Convolutional NetworkYaqin Zhang0Jiayue Han1Binghui Chen2Lin Chang3Ting Song4Guanxiong Cai5Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, ChinaDepartment of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, ChinaDepartment of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, ChinaDepartment of Clinical Laboratory, Children’s Hospital of Nanjing Medical University, Nanjing, ChinaDepartment of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, ChinaSchool of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, ChinaBreast cancer is one of the diseases with the highest incidence and mortality among women in the world, which has posed a serious threat to women’s health. The appearance of clustered calcifications is one of the important signs of breast cancer, and thus how to classify clustered calcifications comes to be a key breakthrough in controlling breast cancer. In this study, the discriminant model based on image convolution is used to learn the image features related to the classification of clustered microcalcifications, and the graph convolutional network (GCN) based on topological graph is used to learn the spatial distribution characteristics of clustered microcalcifications. These two models are fused to obtain a complementary model of image information and spatial information. The results show that the performance of the fusion model proposed in this paper is obviously superior to that of the two classification models in the classification of clustered microcalcification.https://www.frontiersin.org/articles/10.3389/fonc.2022.871662/fullbreast cancermicrocalcificationgraph convolutional networkcomputer-aided diagnosisclassification |
| spellingShingle | Yaqin Zhang Jiayue Han Binghui Chen Lin Chang Ting Song Guanxiong Cai Classification of Microcalcification Clusters Using Bilateral Features Based on Graph Convolutional Network Frontiers in Oncology breast cancer microcalcification graph convolutional network computer-aided diagnosis classification |
| title | Classification of Microcalcification Clusters Using Bilateral Features Based on Graph Convolutional Network |
| title_full | Classification of Microcalcification Clusters Using Bilateral Features Based on Graph Convolutional Network |
| title_fullStr | Classification of Microcalcification Clusters Using Bilateral Features Based on Graph Convolutional Network |
| title_full_unstemmed | Classification of Microcalcification Clusters Using Bilateral Features Based on Graph Convolutional Network |
| title_short | Classification of Microcalcification Clusters Using Bilateral Features Based on Graph Convolutional Network |
| title_sort | classification of microcalcification clusters using bilateral features based on graph convolutional network |
| topic | breast cancer microcalcification graph convolutional network computer-aided diagnosis classification |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.871662/full |
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