Leveraging Fixed and Dynamic Pseudo-labels for Semi-supervised Medical Image Segmentation
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arXiv
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| Natura: | Preprint |
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2024
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| author | Kumari, Suruchi Singh, Pravendra |
| author_facet | Kumari, Suruchi Singh, Pravendra |
| contents | Semi-supervised medical image segmentation has gained growing interest due to its ability to utilize unannotated data. The current state-of-the-art methods mostly rely on pseudo-labeling within a co-training framework. These methods depend on a single pseudo-label for training, but these labels are not as accurate as the ground truth of labeled data. Relying solely on one pseudo-label often results in suboptimal results. To this end, we propose a novel approach where multiple pseudo-labels for the same unannotated image are used to learn from the unlabeled data: the conventional fixed pseudo-label and the newly introduced dynamic pseudo-label. By incorporating multiple pseudo-labels for the same unannotated image into the co-training framework, our approach provides a more robust training approach that improves model performance and generalization capabilities. We validate our novel approach on three semi-supervised medical benchmark segmentation datasets, the Left Atrium dataset, the Pancreas-CT dataset, and the Brats-2019 dataset. Our approach significantly outperforms state-of-the-art methods over multiple medical benchmark segmentation datasets with different labeled data ratios. We also present several ablation experiments to demonstrate the effectiveness of various components used in our approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07256 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Leveraging Fixed and Dynamic Pseudo-labels for Semi-supervised Medical Image Segmentation Kumari, Suruchi Singh, Pravendra Image and Video Processing Computer Vision and Pattern Recognition Semi-supervised medical image segmentation has gained growing interest due to its ability to utilize unannotated data. The current state-of-the-art methods mostly rely on pseudo-labeling within a co-training framework. These methods depend on a single pseudo-label for training, but these labels are not as accurate as the ground truth of labeled data. Relying solely on one pseudo-label often results in suboptimal results. To this end, we propose a novel approach where multiple pseudo-labels for the same unannotated image are used to learn from the unlabeled data: the conventional fixed pseudo-label and the newly introduced dynamic pseudo-label. By incorporating multiple pseudo-labels for the same unannotated image into the co-training framework, our approach provides a more robust training approach that improves model performance and generalization capabilities. We validate our novel approach on three semi-supervised medical benchmark segmentation datasets, the Left Atrium dataset, the Pancreas-CT dataset, and the Brats-2019 dataset. Our approach significantly outperforms state-of-the-art methods over multiple medical benchmark segmentation datasets with different labeled data ratios. We also present several ablation experiments to demonstrate the effectiveness of various components used in our approach. |
| title | Leveraging Fixed and Dynamic Pseudo-labels for Semi-supervised Medical Image Segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.07256 |