TL;DR
A dual U-shaped network based on convolutional neural network and vision transformer is designed to sufficiently achieve the cross-modal feature fusion of image and text to compensate for the defects of existing datasets.
Experimental results show that TDU-Net outperforms other methods in both segmentation accuracy and generalization, and significantly improving clinical diagnosis efficiency and accuracy.
MRD-UNet provides a practical balance between segmentation accuracy and computational efficiency and outperforms baseline CNNs and performs comparably to heavier transformer-based models while using significantly fewer parameters.
Accurate medical image segmentation plays a vital role in clinical diagnostics by facilitating the precise delineation of anatomical structures and pathological regions. However, the performance of existing segmentation methods is often constrained by the scarcity of high-quality annotated datasets, as manual labeling...
Medical images provide essential information for diagnosing and monitoring various diseases and systemic disorders. With advancements in deep learning and neural networks, numerous methods have been proposed to achieve high-level medical image segmentation results. However, the variability of tiny structures and their...
Experiments demonstrate that the proposed cross-layer semantic alignment mechanism outperforms mainstream approaches in terms of Dice, HD95, and Intersection over Union metrics, validating the effectiveness of the cross-layer semantic alignment mechanism for complex medical image segmentation tasks.
A protocol for constructing, training, and evaluating a Multi-view Vision Mamba U-Shaped Network framework for two-dimensional medical image segmentation that achieves competitive segmentation performance using standard evaluation metrics is presented.
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