Dual-Stream CoAtNet models for accurate breast ultrasound image segmentation

The CoAtNet deep neural model has been shown to achieve state-of-the-art performance by stacking convolutional and self-attention layers. In particular, the initial layers of CoAtNet apply efficient convolutions for extracting local features out of the input image and the initial fine-resolution feature maps. In turn, the final layers apply more cumbersome Transformers in order to extract global features from the coarse-resolution feature maps. The model’s outcome directly depends on those final global features. This paper proposes an extension of the original CoAtNet model based on the introduction of a dual stream of convolution and self-attention blocks applied at the final layers of CoAtNet. In this way, those final layers automatically aggregate both local and global features extracted from the initial feature maps. Two dual-stream topologies have been proposed and evaluated. This Dual-Stream CoAtNet model exhibits a significant improvement on the segmentation accuracy of breast ultrasound images, thus contributing to the development of more robust tumor detection methods.