Related Experiment Video For Focal nodular hyperplasia
Updated: May 5, 2026
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
GICAF-Net: A cross-attentional graph-image fusion network for hyperspectral pathological diagnosis of FNH and HCC
Yunze Li1, Haiyan Chen2, Baoxian Gong3
1Department of Biomedical Engineering Institute, School of Control Science and Engineering, Shandong University, Jinan 250061, China.
Background And Objective:
Accurate intraoperative differentiation between focal nodular hyperplasia (FNH) and hepatocellular carcinoma (HCC) remains a major clinical challenge, especially in atypical cases where conventional imaging and histopathology are constrained by turnaround time, cost, or spectral resolution. This study aims to develop a novel deep learning framework to improve the precision and efficiency of hyperspectral pathological diagnosis for liver tumors.
Methods:
We propose GICAF-Net, a Graph-Image Cross-Attentional Fusion Network, designed to leverage hyperspectral imaging (HSI) for capturing fine-grained spatial-spectral information. The network employs a dual-branch architecture: (1) a residual convolutional branch for extracting pseudo-color image features, and (2) a residual graph convolutional branch for modeling topological spatial-spectral features. A Topology-Aware Cross-Attention Fusion (TACA) module enables bidirectional information exchange between the two modalities, while a multi-constraint fusion loss-combining cross-entropy, prediction confidence, and cross-modal attention consistency-enhances classification stability. A balanced hyperspectral liver tumor dataset consisting of 60 HCC and 60 FNH cases was constructed and evaluated using ten-fold cross-validation.
Results:
GICAF-Net achieved an AUC of 0.9571 ± 0.0068, accuracy of 88.34 % ± 1.10 %, and F1-score of 88.32 % ± 1.11 %, outperforming state-of-the-art baseline models. Ablation experiments further validated the contributions of both the TACA module and the multi-constraint loss function in enhancing cross-modal fusion and improving classification performance.
Conclusion:
The integration of graph-based spectral-structural modeling with deep visual features through cross-attention provides a powerful approach for hyperspectral pathological diagnosis. The proposed GICAF-Net demonstrates strong potential for rapid, accurate, and minimally invasive intraoperative differentiation of FNH and HCC, offering valuable clinical support in liver tumor surgery.
