FI-Net: Rethinking Feature Interactions for Medical Image Segmentation

Abstract

To solve the problems of existing hybrid networks based on convolutional neural networks (CNN) and Transformers, we propose a new encoder–decoder network FI-Net based on CNN-Transformer for medical image segmentation. In the encoder part, a dual-stream encoder is used to capture local details and long-range dependencies. Moreover, the attentional feature fusion module is used to perform interactive feature fusion of dual-branch features, maximizing the retention of local details and global semantic information in medical images. At the same time, the multi-scale feature aggregation module is used to aggregate local information and capture multi-scale context to mine more semantic details. The multi-level feature bridging module is used in skip connections to bridge multi-level features and mask information to assist multi-scale feature interaction. Experimental results on seven public medical image datasets fully demonstrate the effectiveness and advancement of our method. In future work, we plan to extend FI-Net to support 3D medical image segmentation tasks and combine self-supervised learning and knowledge distillation to alleviate the overfitting problem of limited data training.