Enhancing Brain Tumor Classification and Generalization Using DDPM-Generated MRI, Mutual Information and Ensemble Learning

Background Deep generative models can improve the generalization of deep learning in medical imaging by enriching limited training data with diverse, realistic synthetic images. Purpose To assess whether Denoising Diffusion Probabilistic Models (DDPM) generated synthetic MRI, with and without mutual information (MI) regularization, enhances brain tumor classification across heterogeneous datasets. Study Type Retrospective. Population A total of 559 patients with low and high grade brain tumors (LGG, HGG) were included from two datasets: public dataset (BraTS, n = 335) and clinical dataset (TASMC, n = 224), used exclusively to evaluate model generalization. Field Strength/Sequence 1.5 T/3.0T-MR / T1WI, T1WI + C, T2WI, and FLAIR images. Assessment DDPM models were trained to generate synthetic MR images of low grade glioma (LGG) and high grade glioma (HGG), with a variant incorporating MI. Image quality was assessed using Pearson-correlation, Frechet-Inception-Distance (FID) and Inception-Score (IS). For classification purposes. For classification, a 2D ResNet-152 was trained under four setups: (1) real images (baseline), (2) +augmentation, (3) +DDPM, and (4) +DDPM + MI. Performance was assessed by accuracy and F1-score. Robustness was tested through cross-dataset evaluation using a 5-fold ensemble. Results The DDPM models, with and without MI, generated high-quality synthetic images, achieving FID = 31.47, 45.00, and IS = 1.50, 1.25, respectively. Lower FID and higher IS indicate enhanced realism and diversity, suggesting that MI improved both the quality and variability of the generated images. Cross-dataset evaluation demonstrated that DDPMs with MI achieved superior generalization performance in brain tumor classification task, with accuracies of 0.89 and 0.85 for BraTS-to-TAMSC and TAMSC-to-BraTS evaluations, respectively. These results outperform the baseline model (0.87, 0.80), traditional data augmentation (0.85, 0.78), and the standard DDPM without MI (0.82, 0.83). Data Conclusion DDPM + MI with ensemble learning significantly improves brain tumor generalization across diverse datasets, consistently outperforming baseline, traditional augmentation, and standard DDPM. This combination offers a robust solution for cross-institutional clinical applications.