Class Imbalanced Medical Image Classification Based on Semi-Supervised Federated Learning
In recent years, the application of federated learning to medical image classification has received much attention and achieved some results in the study of semi-supervised problems, but there are problems such as the lack of thorough study of labeled data, and serious model degradation in the case...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/4/2109 |
| _version_ | 1797622669111197696 |
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| author | Wei Liu Jiaqing Mo Furu Zhong |
| author_facet | Wei Liu Jiaqing Mo Furu Zhong |
| author_sort | Wei Liu |
| collection | DOAJ |
| description | In recent years, the application of federated learning to medical image classification has received much attention and achieved some results in the study of semi-supervised problems, but there are problems such as the lack of thorough study of labeled data, and serious model degradation in the case of small batches in the face of the data category imbalance problem. In this paper, we propose a federated learning method using a combination of regularization constraints and pseudo-label construction, where the federated learning framework consists of a central server and local clients containing only unlabeled data, and labeled data are passed from the central server to each local client to take part in semi-supervised training. We first extracted the class imbalance factors from the labeled data to participate in the training to achieve label constraints, and secondly fused the labeled data with the unlabeled data at the local client to construct augmented samples, looped through to generate pseudo-labels. The purpose of combining these two methods is to select fewer classes with higher probability, thus providing an effective solution to the class imbalance problem and improving the sensitivity of the network to unlabeled data. We experimentally validated our method on a publicly available medical image classification data set consisting of 10,015 images with small batches of data. Our method improved the AUC by 7.35% and the average class sensitivity by 1.34% compared to the state-of-the-art methods, which indicates that our method maintains a strong learning capability even with an unbalanced data set with fewer batches of trained models. |
| first_indexed | 2024-03-11T09:13:33Z |
| format | Article |
| id | doaj.art-edc399a814ca49f3aae7029c96842622 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:13:33Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-edc399a814ca49f3aae7029c968426222023-11-16T18:51:02ZengMDPI AGApplied Sciences2076-34172023-02-01134210910.3390/app13042109Class Imbalanced Medical Image Classification Based on Semi-Supervised Federated LearningWei Liu0Jiaqing Mo1Furu Zhong2Xinjiang Key Laboratory of Signal Detection and Processing, College of Information Science and Engineering, Xinjiang University, Urumqi 830017, ChinaXinjiang Key Laboratory of Signal Detection and Processing, College of Information Science and Engineering, Xinjiang University, Urumqi 830017, ChinaSchool of Physics and Electronic Science, Zunyi Normal College, Zunyi 563006, ChinaIn recent years, the application of federated learning to medical image classification has received much attention and achieved some results in the study of semi-supervised problems, but there are problems such as the lack of thorough study of labeled data, and serious model degradation in the case of small batches in the face of the data category imbalance problem. In this paper, we propose a federated learning method using a combination of regularization constraints and pseudo-label construction, where the federated learning framework consists of a central server and local clients containing only unlabeled data, and labeled data are passed from the central server to each local client to take part in semi-supervised training. We first extracted the class imbalance factors from the labeled data to participate in the training to achieve label constraints, and secondly fused the labeled data with the unlabeled data at the local client to construct augmented samples, looped through to generate pseudo-labels. The purpose of combining these two methods is to select fewer classes with higher probability, thus providing an effective solution to the class imbalance problem and improving the sensitivity of the network to unlabeled data. We experimentally validated our method on a publicly available medical image classification data set consisting of 10,015 images with small batches of data. Our method improved the AUC by 7.35% and the average class sensitivity by 1.34% compared to the state-of-the-art methods, which indicates that our method maintains a strong learning capability even with an unbalanced data set with fewer batches of trained models.https://www.mdpi.com/2076-3417/13/4/2109federated learningsemi-supervised algorithmpseudo-labelsclassification |
| spellingShingle | Wei Liu Jiaqing Mo Furu Zhong Class Imbalanced Medical Image Classification Based on Semi-Supervised Federated Learning Applied Sciences federated learning semi-supervised algorithm pseudo-labels classification |
| title | Class Imbalanced Medical Image Classification Based on Semi-Supervised Federated Learning |
| title_full | Class Imbalanced Medical Image Classification Based on Semi-Supervised Federated Learning |
| title_fullStr | Class Imbalanced Medical Image Classification Based on Semi-Supervised Federated Learning |
| title_full_unstemmed | Class Imbalanced Medical Image Classification Based on Semi-Supervised Federated Learning |
| title_short | Class Imbalanced Medical Image Classification Based on Semi-Supervised Federated Learning |
| title_sort | class imbalanced medical image classification based on semi supervised federated learning |
| topic | federated learning semi-supervised algorithm pseudo-labels classification |
| url | https://www.mdpi.com/2076-3417/13/4/2109 |
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