Effective and Efficient Medical Image Segmentation with Hierarchical Context Interaction

The U-Net models have become the predominant architecture within the domain of medical image segmentation. Recent advancements have showcased the potential of incorporating attention-based techniques into U-Net structures. Nevertheless, the inclusion of attention mechanisms often leads to a substantial increase in both computational demands and the number of parameters, with only a marginal improvement in the performance. This observation raises a critical evaluation of the efficiency associated with the integration of attention modules. In this paper, we propose a novel methodology termed Hierarchical Context Interaction (HCI), a parameter-efficient, attention-free enhancement that can be seamlessly incorporated into U-Net-based models. Experimental results demonstrate that our proposed HCI module attains state-of-the-art performance on two widely used benchmarks, i.e. Medical Segmentation Decathlon Datasets and Synapse Datasets, while concurrently sustaining a computationally efficient profile comparable to conventional U-Net configurations.