CFNet: A medical image segmentation method using the multi-view attention mechanism and adaptive fusion strategy


Computer Science

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The image feature extraction method based on the attention mechanism has contributed significantly to the accuracy of medical image segmentation. However, the current attention mechanism is based on the single-view information for feature extraction which imposes certain limitations for extracting efficient features. In this study, we propose the encoder-decoder structure of the U-Net as the basic network structure to construct a medical image segmentation method based on the multi-view attention mechanism and adaptive fusion strategy. We will refer to this new network as CFNet. The first component of CFNet is a cross-scale feature fusion method (CFF) employing a new multi-view attention mechanism (MAM) for feature extraction. This can effectively extract features in the multi-receptive field space and obtain more effective cross-scale fusion features in skip-connection. The second component is a fusion weight adaptive allocation strategy (FAS), which can guide the cross-scale fusion features to effectively connect to the decoder features for solving the semantic gap. We evaluated the CFNet using two publicly available medical image segmentation datasets: MoNuSeg and LGG. The experimental results show that the CFNet can achieve better performance compared with the current state-of-the-art methods in medical image segmentation. We then perform extensive ablation studies to validate our method.

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Biomedical Signal Processing and Control

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