The automatic segmentation of medical images has made continuous progress due to the development of convolutional neural networks (CNNs) and attention mechanism. However, previous works usually explore the attention features of a certain dimension in the image, thus may ignore the correlation between feature maps in other dimensions. Therefore, how to capture the global features of various dimensions is still facing challenges. To deal with this problem, we propose a triple attention network (TA-Net) by exploring the ability of the attention mechanism to simultaneously recognize global contextual information in the channel domain, spatial domain, and feature internal domain. Specifically, during the encoder step, we propose a channel with self-attention encoder (CSE) block to learn the long-range dependencies of pixels. The CSE effectively increases the receptive field and enhances the representation of target features. In the decoder step, we propose a spatial attention up-sampling (SU) block that makes the network pay more attention to the position of the useful pixels when fusing the low-level and high-level features. Extensive experiments were tested on four public datasets and one local dataset. The datasets include the following types: retinal blood vessels (DRIVE and STARE), cells (ISBI 2012), cutaneous melanoma (ISIC 2017), and intracranial blood vessels. Experimental results demonstrate that the proposed TA-Net is overall superior to previous state-of-the-art methods in different medical image segmentation tasks with high accuracy, promising robustness, and relatively low redundancy.
Keywords: Channel attention; Deep learning; Medical image segmentation; Self-attention; Spatial attention.
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