A hierarchical fusion strategy of deep learning networks for detection and segmentation of hepatocellular carcinoma from computed tomography images.
I-Cheng LeeYung-Ping TsaiYen-Cheng LinTing-Chun ChenChia-Heng YenNai-Chi ChiuHsuen-En HwangChien-An LiuJia-Guan HuangRheun-Chuan LeeYee ChaoShinn-Ying HoYi-Hsiang HuangPublished in: Cancer imaging : the official publication of the International Cancer Imaging Society (2024)
The HFS-Net model achieved good performance in the detection and segmentation of HCC from dynamic CT images, which may support radiologic diagnosis and facilitate automatic radiomics analysis.
Keyphrases
- deep learning
- convolutional neural network
- computed tomography
- artificial intelligence
- contrast enhanced
- loop mediated isothermal amplification
- machine learning
- positron emission tomography
- image quality
- dual energy
- real time pcr
- magnetic resonance imaging
- label free
- squamous cell carcinoma
- lymph node metastasis
- data analysis
- pet ct