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. Author manuscript; available in PMC: 2022 Apr 1.
Published in final edited form as: Comput Med Imaging Graph. 2021 Mar 11;89:101894. doi: 10.1016/j.compmedimag.2021.101894

Figure 2-3:

Figure 2-3:

The deep learning architecture of the hepatocellular cancer (HCC) recurrence risk assessment system. We developed the i-RAPIT model with two CapsNet networks, one Radial Basis Function (RBF) neural network and one multilayer perceptron. The first CapsNet networks is used for compressing the features from the original magnetic resonance (MR) images. The second CapsNet network is used for compressing the features from the original pathological images. A Natural Language Processing (NLP) tool is used to combine with imaging and demographic features as the input of the multiple feature RBF network, whose compressed features are used as input on the final decision- making multilayer perceptron. The output of the multilayer perceptron is the final assessment of HCC recurrence risk.