DMCGNet: A Novel Network for Medical Image Segmentation With Dense Self-Mimic and Channel Grouping Mechanism

Automatic Medical Image Segmentation (MIS) can assist doctors by reducing labor and providing a unified standard. Nowadays, approaches based on Deep Learning have become mainstream for MIS because of their ability of automatic feature extraction. However, due to the plain network design and targets variety in medical images, the semantic features can hardly be extracted adequately. In this work, we propose a novel Dense Self-Mimic and Channel Grouping based Network (DMCGNet) for MIS for better feature extraction. Specifically, we introduce a Pyramid Target-aware Dense Self Mimic (PTDSM) module, which is capable of exploring deeper and better feature representation with no parameter increase. Then, to utilize features efficiently, an effective Channel Split based Feature Fusion Module (CSFFM) is proposed for feature reuse, which strengthens the adaptation of multi-scale targets by utilizing the channel grouping mechanism. Finally, to train the proposed method adequately, Deep Supervision with Group Ensemble Learning (DSGEL) is equipped to the network. Extensive experiments demonstrate that our proposed model achieves state-of-the-art performance on 4 medical image segmentation datasets.