CAFCT: Contextual and Attentional Feature Fusions of Convolutional Neural Networks and Transformer for Liver Tumor Segmentation. (arXiv:2401.16886v1 [cs.CV])

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. …

arxiv cnn convolutional neural network convolutional neural networks cs.cv feature hybrid identify image medical network networks neural network neural networks paper scans segmentation semantic transformer tumors