Leveraging Fixed and Dynamic Pseudo-labels for Semi-supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2405.07256v1
• Date: Sun, 12 May 2024 11:30:01 GMT
• Title: Leveraging Fixed and Dynamic Pseudo-labels for Semi-supervised Medical Image Segmentation
• Authors: Suruchi Kumari, Pravendra Singh,
• Abstract summary: 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. We propose a novel approach where multiple pseudo-labels for the same unannotated image are used to learn from the unlabeled data.
• Score: 7.9449756510822915
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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