Dual-branch collaborative GAN with multi-scale CBAM and anatomical topology coding for enhanced early HCC detection in CT

The early detection of Hepatocellular Carcinoma (HCC) using computed tomography (CT) is impeded by high annotation costs, lesion heterogeneity, and inadequate anatomical topology modeling. This study proposes a Dual-Branch Collaborative Generative Adversarial Network (DB-Collab GAN) with Anatomical Topology Coding to address these challenges. The framework features a dual-branch architecture that forms a "segmentation-guided detection" loop, with cross-layer feature sharing enhancing local-global complementarity. A layered Multi-Scale Convolutional Block Attention Module (CBAM) captures micro-details via 1 × 1 convolutions and liver anatomy via 5 × 5 convolutions. Anatomically tailored sine-cosine coding embeds the Couinaud segment topology, reducing the mean localization error (ADE) to 3.01 mm. Semi-supervised adversarial optimization with a dual-path discriminator achieved performance comparable to 7,140 supervised cases using only 1,070 labeled cases. On 7,140 clinical CT slices, the method outperformed the baselines in terms of accuracy (0.8875 ± 0.02), recall (0.8613 ± 0.03), and F1-score (0.8848 ± 0.02), with a 10.66% higher F1-score than Mask RCNN. Ablation studies confirmed the contributions of the multiscale CBAM and topology coding. It maintains robustness under high noise (ADE = 4.57 mm), providing a low-annotation-dependent solution, effectively reducing missed diagnoses and misclassifications of small lesions and vascular artifacts, and supporting clinical decision-making in early intervention.