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This is a Human Clinical Trial study.

September 10, 2025Frontiers in OncologyOpen Access

SMF-net: semantic-guided multimodal fusion network for precise pancreatic tumor segmentation in medical CT image

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Proposed framework enhances pancreatic tumor segmentation in CT images, improving accuracy and local feature retention.

The model achieved up to 79.25% Dice score for pancreas segmentation and 64.21% for tumor segmentation.
SMF-Net integrates a CNN-Transformer hybrid encoder with a progressive feature extraction module.
The framework utilizes a semi-supervised learning paradigm to tackle multimodal data scarcity effectively.
Experimental results showed that SMF-Net outperformed existing methods in both pancreatic and pulmonary segmentation.

Zhou et al. (2025) studied this question.

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