The key highlights and insights from the content are:
Medical image segmentation is essential for clinical applications like disease diagnosis, treatment planning, and quantification. However, it is challenging due to the need for accurate segmentation of regions of interest.
Convolutional neural networks (CNNs) like U-Net and its variants have shown strong performance in medical image segmentation, but they are limited in capturing long-range dependencies. Transformer-based models can effectively model long-range dependencies, but they suffer from high computational complexity.
The proposed BRAU-Net++ is a hybrid CNN-Transformer network that combines the strengths of both approaches. It uses a bi-level routing attention mechanism as the core building block to design a U-shaped encoder-decoder structure, which can learn global semantic information while reducing computational complexity.
BRAU-Net++ also restructures the skip connection by incorporating channel-spatial attention, implemented using convolution operations, to minimize local spatial information loss and amplify global dimension-interaction of multi-scale features.
Extensive experiments on three diverse medical imaging datasets (Synapse multi-organ segmentation, ISIC-2018 Challenge, and CVC-ClinicDB) demonstrate that BRAU-Net++ outperforms other state-of-the-art methods, including its baseline BRAU-Net, under almost all evaluation metrics, showcasing its generality and robustness for multi-modal medical image segmentation.
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by Libin Lan, P... um arxiv.org 10-01-2024
https://arxiv.org/pdf/2401.00722.pdfTiefere Fragen