CGHNet: Cross-Guided 2D–3D Hybrid Network with attention mechanism for focal liver lesion classification

Abstract

Accurate differential diagnosis of focal liver lesions (FLLs) is pivotal for clinical treatment planning and prognosis. Although multi-sequence MRI serves as the primary imaging modality, precise characterization remains challenging due to complex lesion heterogeneity and subtle inter-class similarities. Existing computational models face an inherent dilemma: slice-based 2D models lack Z-axis spatial continuity, whereas volume-based 3D models often compromise intricate intra-slice semantics to capture global structures. To overcome these limitations, we propose the Cross-Guided 2D–3D Hybrid Network (CGHNet). CGHNet effectively synergizes these complementary paradigms by employing a 2D Vision Transformer (ViT) to model long-range intra-slice dependencies and a 3D CNN to preserve holistic volumetric contexts. To resolve the severe semantic misalignment between these heterogeneous representations, we introduce a Cross-Guided Fusion Mechanism (CGFM). This module employs a reciprocal gating strategy that utilizes macroscopic global contexts from one branch to dynamically suppress background noise and recalibrate feature responses in the other. Furthermore, an Adaptive Decision Fusion (ADF) module is designed to function as an instance-aware arbiter, selectively weighting branch-specific predictions via residual correction to robustly rectify diagnostic uncertainties. Extensive experiments on the LLD-MMRI2023 dataset demonstrate that CGHNet achieves competitive performance, yielding an accuracy of 82.7% and an AUC of 96.5%. These results compare favorably with reproduced representative baselines under the same experimental setting, highlighting the potential of CGHNet as an assistive tool for focal liver lesion classification.