SPEMix: a lightweight method via superclass pseudo-label and efficient mixup for echocardiogram view classification
IntroductionIn clinical, the echocardiogram is the most widely used for diagnosing heart diseases. Different heart diseases are diagnosed based on different views of the echocardiogram images, so efficient echocardiogram view classification can help cardiologists diagnose heart disease rapidly. Echo...
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Frontiers Media S.A.
2025-01-01
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| author | Shizhou Ma Yifeng Zhang Delong Li Yixin Sun Zhaowen Qiu Lei Wei Suyu Dong |
| author_facet | Shizhou Ma Yifeng Zhang Delong Li Yixin Sun Zhaowen Qiu Lei Wei Suyu Dong |
| author_sort | Shizhou Ma |
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| description | IntroductionIn clinical, the echocardiogram is the most widely used for diagnosing heart diseases. Different heart diseases are diagnosed based on different views of the echocardiogram images, so efficient echocardiogram view classification can help cardiologists diagnose heart disease rapidly. Echocardiogram view classification is mainly divided into supervised and semi-supervised methods. The supervised echocardiogram view classification methods have worse generalization performance due to the difficulty of labeling echocardiographic images, while the semi-supervised echocardiogram view classification can achieve acceptable results via a little labeled data. However, the current semi-supervised echocardiogram view classification faces challenges of declining accuracy due to out-of-distribution data and is constrained by complex model structures in clinical application.MethodsTo deal with the above challenges, we proposed a novel open-set semi-supervised method for echocardiogram view classification, SPEMix, which can improve performance and generalization by leveraging out-of-distribution unlabeled data. Our SPEMix consists of two core blocks, DAMix Block and SP Block. DAMix Block can generate a mixed mask that focuses on the valuable regions of echocardiograms at the pixel level to generate high-quality augmented echocardiograms for unlabeled data, improving classification accuracy. SP Block can generate a superclass pseudo-label of unlabeled data from the perspective of the superclass probability distribution, improving the classification generalization by leveraging the superclass pseudolabel.ResultsWe also evaluate the generalization of our method on the Unity dataset and the CAMUS dataset. The lightweight model trained with SPEMix can achieve the best classification performance on the publicly available TMED2 dataset.DiscussionFor the first time, we applied the lightweight model to the echocardiogram view classification, which can solve the limits of the clinical application due to the complex model architecture and help cardiologists diagnose heart diseases more efficiently. |
| format | Article |
| id | doaj-art-305930b91bcf48948b67c528548dc7ad |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-305930b91bcf48948b67c528548dc7ad2025-01-08T06:12:16ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-01-01710.3389/frai.2024.14672181467218SPEMix: a lightweight method via superclass pseudo-label and efficient mixup for echocardiogram view classificationShizhou Ma0Yifeng Zhang1Delong Li2Yixin Sun3Zhaowen Qiu4Lei Wei5Suyu Dong6College of Aulin, Northeast Forestry University, Harbin, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin, ChinaThe Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin, ChinaDepartment of Cardiovascular Surgery, First Affiliated Hospital With Nanjing Medical University, Nanjing, ChinaCollege of Computer and Control Engineering, Northeast Forestry University, Harbin, ChinaIntroductionIn clinical, the echocardiogram is the most widely used for diagnosing heart diseases. Different heart diseases are diagnosed based on different views of the echocardiogram images, so efficient echocardiogram view classification can help cardiologists diagnose heart disease rapidly. Echocardiogram view classification is mainly divided into supervised and semi-supervised methods. The supervised echocardiogram view classification methods have worse generalization performance due to the difficulty of labeling echocardiographic images, while the semi-supervised echocardiogram view classification can achieve acceptable results via a little labeled data. However, the current semi-supervised echocardiogram view classification faces challenges of declining accuracy due to out-of-distribution data and is constrained by complex model structures in clinical application.MethodsTo deal with the above challenges, we proposed a novel open-set semi-supervised method for echocardiogram view classification, SPEMix, which can improve performance and generalization by leveraging out-of-distribution unlabeled data. Our SPEMix consists of two core blocks, DAMix Block and SP Block. DAMix Block can generate a mixed mask that focuses on the valuable regions of echocardiograms at the pixel level to generate high-quality augmented echocardiograms for unlabeled data, improving classification accuracy. SP Block can generate a superclass pseudo-label of unlabeled data from the perspective of the superclass probability distribution, improving the classification generalization by leveraging the superclass pseudolabel.ResultsWe also evaluate the generalization of our method on the Unity dataset and the CAMUS dataset. The lightweight model trained with SPEMix can achieve the best classification performance on the publicly available TMED2 dataset.DiscussionFor the first time, we applied the lightweight model to the echocardiogram view classification, which can solve the limits of the clinical application due to the complex model architecture and help cardiologists diagnose heart diseases more efficiently.https://www.frontiersin.org/articles/10.3389/frai.2024.1467218/fullsuperclass pseudo-labellightweightsemi-supervisedopen-setechocardiogram view classification |
| spellingShingle | Shizhou Ma Yifeng Zhang Delong Li Yixin Sun Zhaowen Qiu Lei Wei Suyu Dong SPEMix: a lightweight method via superclass pseudo-label and efficient mixup for echocardiogram view classification Frontiers in Artificial Intelligence superclass pseudo-label lightweight semi-supervised open-set echocardiogram view classification |
| title | SPEMix: a lightweight method via superclass pseudo-label and efficient mixup for echocardiogram view classification |
| title_full | SPEMix: a lightweight method via superclass pseudo-label and efficient mixup for echocardiogram view classification |
| title_fullStr | SPEMix: a lightweight method via superclass pseudo-label and efficient mixup for echocardiogram view classification |
| title_full_unstemmed | SPEMix: a lightweight method via superclass pseudo-label and efficient mixup for echocardiogram view classification |
| title_short | SPEMix: a lightweight method via superclass pseudo-label and efficient mixup for echocardiogram view classification |
| title_sort | spemix a lightweight method via superclass pseudo label and efficient mixup for echocardiogram view classification |
| topic | superclass pseudo-label lightweight semi-supervised open-set echocardiogram view classification |
| url | https://www.frontiersin.org/articles/10.3389/frai.2024.1467218/full |
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