CAFCT: Contextual and Attentional Feature Fusions of Convolutional
  Neural Networks and Transformer for Liver Tumor Segmentation
Ming Kang Chee-Ming Ting Fung Fung Ting Raphaël Phan
Medical image semantic segmentation techniques can help identify tumors
automatically from computed tomography (CT) scans. In this paper, we propose a
Contextual and Attentional feature Fusions enhanced Convolutional Neural
Network (CNN) and Transformer hybrid network (CAFCT) model for liver tumor
segmentation. In the proposed model, three other modules are introduced in the
network architecture: Attentional Feature Fusion (AFF), Atrous Spatial Pyramid
Pooling (ASPP) of DeepLabv3, and Attention Gates (AGs) to improve contextual
information related to tumor boundaries for accurate segmentation. Experimental
results show that the proposed CAFCT achieves a mean Intersection over Union
(IoU) of 90.38% and Dice score of 86.78%, respectively, on the Liver Tumor
Segmentation Benchmark (LiTS) dataset, outperforming pure CNN or Transformer
methods, e.g., Attention U-Net, and PVTFormer.