[Multi-scale medical image segmentation based on pixel encoding and spatial attention mechanism].

In response to the issues of single-scale information loss and large model parameter size during the sampling process in U-Net and its variants for medical image segmentation, this paper proposes a multi-scale medical image segmentation method based on pixel encoding and spatial attention. Firstly, by redesigning the input strategy of the Transformer structure, a pixel encoding module is introduced to enable the model to extract global semantic information from multi-scale image features, obtaining richer feature information. Additionally, deformable convolutions are incorporated into the Transformer module to accelerate convergence speed and improve module performance. Secondly, a spatial attention module with residual connections is introduced to allow the model to focus on the foreground information of the fused feature maps. Finally, through ablation experiments, the network is lightweighted to enhance segmentation accuracy and accelerate model convergence. The proposed algorithm achieves satisfactory results on the Synapse dataset, an official public dataset for multi-organ segmentation provided by the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), with Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95) scores of 77.65 and 18.34, respectively. The experimental results demonstrate that the proposed algorithm can enhance multi-organ segmentation performance, potentially filling the gap in multi-scale medical image segmentation algorithms, and providing assistance for professional physicians in diagnosis.

PMJAF-Net: Pyramidal multi-scale joint attention and adaptive fusion network for explainable skin lesion segmentation.

Traditional convolutional neural networks have achieved remarkable success in skin lesion segmentation. However, the successive pooling operations and convolutional spans reduce the feature resolution and hinder the dense prediction for spatial information, resulting in blurred boundaries, low accuracy and poor interpretability for irregular lesion segmentation under low contrast. To solve the above issues, a pyramidal multi-scale joint attention and adaptive fusion network for explainable (PMJAF-Net) skin lesion segmentation is proposed. Firstly, an adaptive spatial attention module is designed to establish the long-term correlation between pixels, enrich the global and local contextual information, and refine the detailed features. Subsequently, an efficient pyramidal multi-scale channel attention module is proposed to capture the multi-scale information and edge features by using the pyramidal module. Meanwhile, a channel attention module is devised to establish the long-term correlation between channels and highlight the most related feature channels to capture the multi-scale key information on each channel. Thereafter, a multi-scale adaptive fusion attention module is put forward to efficiently fuse the scale features at different decoding stages. Finally, a novel hybrid loss function based on region salient features and boundary quality is presented to guide the network to learn from map-level, patch-level and pixel-level and to accurately predict the lesion regions with clear boundaries. In addition, visualizing attention weight maps are utilized to visually enhance the interpretability of our proposed model. Comprehensive experiments are conducted on four public skin lesion datasets, and the results demonstrate that the proposed network outperforms the state-of-the-art methods, with the segmentation assessment evaluation metrics Dice, JI, and ACC improved to 92.65%, 87.86% and 96.26%, respectively.