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InfoResearchPeer-reviewed
Fully Perturbed Self-Ensemble Framework Using Cascaded Parallel CNN-Transformer for Semisupervised Medical Image Segmentation
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Summary
Researchers propose FPSE, a fully perturbed self-ensemble framework that combines a cascaded parallel CNN-transformer for semisupervised medical image segmentation. The method applies data, model, and feature perturbations and feeds CNN-extracted shallow local features to the transformer, which reduces its need for labeled data. With only 5% labeled data on the automatic cardiac diagnosis challenge and AbdomenCT-1K, and 1% on ISIC2018, FPSE outperforms prior state-of-the-art methods by 11.5%, 0.7% and 2.4% in Dice score, respectively.