Early Follicular Lymphoma Grading via PET-CT Fusion and Bayesian Deep Learning

Accurate grading of follicular lymphoma (FL) is crucial for personalized treatment, but biopsy-based histopathology is invasive and limited by observer variability. To address these limits, we present an artificial intelligence framework for physician-guided assisted FL grading using PET-CT imaging. Our approach integrates an enhanced dual-discriminator conditional GAN (DDCGAN) featuring similarity and chrominance constraints to generate task-specific fused images with preserved metabolic-structural cues. Furthermore, a Bayesian ResNet is introduced to explicitly model predictive uncertainty, effectively resolving classification ambiguity between adjacent FL Grades I and II. Rigorous evaluation on a multi-center dataset of 837 patients, including FL and diffuse large B-cell lymphoma (DLBCL), proves that our framework delivers superior generalizability. It achieves an accuracy of 0.871, precision of 0.875, and macro-F1 of 0.816, outperforming single-modality and existing state-of-the-art fusion models. Ultimately, this task-oriented image fusion and uncertainty-aware framework offers a highly practical, non-invasive decision-support tool to support scalable clinical decision-making in hospital workflows.