Medical image segmentation based on frequency domain decomposition SVD linear attention

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 22, 2025

Convolutional Neural Networks (CNNs) have achieved remarkable segmentation accuracy in medical image tasks. However, the Vision Transformer (ViT) model, with its capability of extracting global information, offers a significant advantage contextual information compared to limited receptive field convolutional kernels CNNs. Despite this, ViT models struggle fully detect and extract high-frequency signals, such as textures boundaries, images. These features are essential imaging, targets like tumors pathological organs exhibit differences texture boundaries across different stages. Additionally, high resolution images leads computational complexity self-attention mechanism ViTs. To address these limitations, we propose network framework based on frequency domain decomposition using Laplacian pyramid. This approach selectively computes attention for signals original enhance spatial structural effectively. During feature computation, introduce Singular Value Decomposition (SVD) an effective representation matrix from image, which is then applied computation process linear projection. method reduces while preserving features. We demonstrated validity superiority our model Abdominal Multi-Organ Segmentation dataset Dermatological Disease dataset, Synapse score 82.68 Dice metrics 17.23 mm HD metrics. Experimental results indicate that consistently exhibits effectiveness improved various datasets.

Language: Английский

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Cross-set data augmentation for semi-supervised medical image segmentation

Image and Vision Computing, Journal Year: 2025, Volume and Issue: 154, P. 105407 - 105407

Published: Jan. 2, 2025

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Neural Networks, Journal Year: 2025, Volume and Issue: 186, P. 107258 - 107258

Published: Feb. 14, 2025

Language: Английский

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Expert guidance and partially-labeled data collaboration for multi-organ segmentation

Neural Networks, Journal Year: 2025, Volume and Issue: unknown, P. 107396 - 107396

Published: March 1, 2025

Language: Английский

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Toward high-quality pseudo masks from noisy or weak annotations for robust medical image segmentation

Neural Networks, Journal Year: 2024, Volume and Issue: 181, P. 106850 - 106850

Published: Nov. 1, 2024

Language: Английский

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Semi-supervised Focal Cortical Dysplasia Lesion Segmentation with Dual Branch Consistency Regularization

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Journal Year: 2024, Volume and Issue: unknown, P. 4315 - 4322

Published: Dec. 3, 2024

Language: Английский

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