CAFCT: Contextual and Attentional Feature Fusions of Convolutional Neural Networks and Transformer for Liver Tumor Segmentation

• URL: http://arxiv.org/abs/2401.16886v1
• Date: Tue, 30 Jan 2024 10:42:11 GMT
• Title: CAFCT: Contextual and Attentional Feature Fusions of Convolutional Neural Networks and Transformer for Liver Tumor Segmentation
• Authors: Ming Kang, Chee-Ming Ting, Fung Fung Ting, Rapha\"el Phan
• Abstract summary: We propose a Contextual and Attentional feature Fusions enhanced Convolutional Network (CNN) and Transformer hybrid network (CAFCT) model for liver tumor segmentation. Experimental results show that the proposed CAFCT achieves semantic Intersection of 90.38% and Dice score of 86.78%, respectively.
• Score: 4.255240258747643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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