Generative AI for Synthetic Neuroimaging Data
- 1. Generative AI for Synthetic Neuroimaging Data Advancing Neuroinformatics with GANs and Synthetic Data Augmentation Presented by: K Sohith Reddy BEng Bioinformatics (IV) Saveetha Institute of Medical And Technical Sciences, India
- 2. The Problem: Data Scarcity in Neuroimaging Deep learning in neuroimaging demands large, annotated datasets Rare neurological conditions (e.g., Huntington's, Rett syndrome) have limited samples Patient privacy and imaging costs further restrict data access Synthetic neuroimaging using generative AI offers a scalable solution
- 3. Generative Adversarial Networks (GANs) Powerful neural networks that learn to generate realistic data through adversarial training Generator Creates synthetic neuroimaging data from random noise vectors Continuously improves through adversarial feedback Discriminator Distinguishes real from synthetic brain scans Acts as quality control for generated images Synthetic Output Realistic 2D, 3D, and 4D brain images indistinguishable from actual scans
- 4. GANs for fMRI and MRI Augmentation MRI Applications T1-weighted, T2, FLAIR data augmented using specialised 3D- GANs fMRI Synthesis Time-series voxel data created with spatio-temporal GAN architectures Conditional Generation cGANs produce images conditioned on age, disease status, or brain region Results: higher model generalisability and reduced overfitting in downstream tasks
- 5. Synthetic Data for Rare Neurological Disorders ALS Motor neuron pathology synthesis enables development of early detection models Progressive Supranuclear Palsy Midbrain atrophy patterns synthesized from limited samples Paediatric Leukodystrophies White matter abnormalities generated to supplement scarce paediatric imaging GANs trained on small datasets (<100 samples) can generate diverse, pathology-preserving images, enabling balanced training sets and disease-specific diagnostic models
- 6. Ensuring Anatomical and Clinical Validity Validation Approaches 1 Visual Turing Tests Expert neuroradiologists assess synthetic scans for realism and clinical plausibility 2 Quantitative Metrics SSIM, PSNR, and Frechet Inception Distance measure statistical similarity to real data 3 Downstream Performance Classification/segmentation accuracy with synthetic training data validates utility Synthetic data must preserve brain structure, lesion morphology, and statistical relationships to be clinically useful
- 7. Training Robust AI Models with Synthetic Data Enhanced Diversity Synthetic data augments diversity and simulates rare pathological scenarios Domain Adaptation Improves robustness to scanner differences and motion artifacts Transfer Learning Enhances few-shot learning capabilities for low-resource clinical settings Reduced Bias Balances class distributions in inherently imbalanced medical datasets
- 8. Case Study: GAN-Augmented Alzheimer's MRI Dataset 8% Accuracy Improvement Using synthetic data for underrepresented early MCI stages 3D ResNet Architecture Deep learning model for staging AD progression 100% Expert Validation Neuroradiologists verified synthetic scan quality ADNI dataset augmented with GAN-generated T1 MRIs for stages with limited samples
- 9. Challenges and Future Research Current Challenges Mode collapse in GANs limiting output diversity Synthetic artifacts potentially biasing learning algorithms Regulatory concerns for clinical AI trained on synthetic data Ensuring preservation of subtle pathological features Future Directions 1 Diffusion Models More stable synthesis with fewer artifacts than traditional GANs 2 Multi-modal Generation Simultaneous synthesis of MRI + PET + EEG for comprehensive analysis 3 Federated Synthesis Privacy-preserving synthetic data sharing between clinical centres
- 10. Conclusion & Takeaways 1 Generative AI addresses critical data bottlenecks in neuroimaging research and clinical applications 2 GANs create realistic, diverse, and clinically useful synthetic brain scans that preserve pathological features 3 Synthetic data significantly improves AI performance, especially for rare disease diagnosis and staging 4 A powerful tool for scalable, ethical, and inclusive neuroscience research with broad clinical applications