Swin-UMamba leverages Mamba blocks with ImageNet pretraining to outperform CNNs, ViTs, and other Mamba-based models in medical image segmentation. The study emphasizes the significance of pretraining for data-efficient analysis in limited medical datasets.
Accurate medical image segmentation is crucial for clinical practice efficiency. Deep learning advancements address local features and global dependencies integration challenges. Swin-UMamba demonstrates superior performance through innovative architecture leveraging pretrained models.
Existing Mamba-based models lack exploration of pretraining benefits, essential for effective medical image analysis. Challenges include transferability from generic vision models and scalability for real-world deployment. Swin-UMamba introduces a novel approach with improved accuracy and efficiency.
The study evaluates Swin-UMamba across diverse datasets, showcasing its superiority over baseline methods in organ, instrument, and cell segmentation tasks. The impact of ImageNet pretraining is evident in enhanced segmentation accuracy and stability across different datasets.
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by Jiarun Liu,H... a las arxiv.org 03-07-2024
https://arxiv.org/pdf/2402.03302.pdfConsultas más profundas