ADMC: An Attention-based Dense Multi-scale Convolutional architecture for Medical Image Segmentation

Medical image segmentation is crucial for many healthcare applications, and deep learning networks have shown great potential in performing semantic segmentation tasks effectively. However, existing methods often suffer from the loss of important local features. To solve this problem, We introduce an innovative approach: a multi-scale and multi-connection feature adaptive fusion method guided by an attention mechanism. The proposed method emphasizes adaptive fusion of multi-scale and multi-connection features, effectively capturing meaningful features at different scales, while also utilizing attention mechanism to enhance feature representation. Specifically, Our approach leverages dense multi-scale skip connections to bridge the semantic gap between the feature maps of the encoder and decoder, where the feature map in each decoder module is connected with each feature map in encoder module. Besides, our network incorporates a deep attention block (DCS) in the encoder to capture meaningful local features. In the decoder, we introduce an efficient multi-scale convolution block (MSDC) to refine feature maps by performing deep convolution across multiple scales. Experimental results in the well known The ISIC dataset results demonstrate that our approach significantly outperforms the baseline method in the task of medical image segmentation.