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Explore how AI and continual learning can revolutionize MRI diagnostics, using our real-world case study in detecting Focal Cortical Dysplasias (FCD) - a crucial factor in epilepsy treatment.

In this session, we’ll dive into how continual learning techniques, inspired by human adaptability, help AI models improve diagnostic accuracy, minimize false positives, and handle evolving data in medical settings. We’ll discuss challenges like catastrophic forgetting, maintaining model performance in dynamic environments, and practical strategies that developers can apply to other domains. Gain hands-on insights into building resilient AI systems that evolve and adapt to new data, ensuring long-term reliability in critical applications.

NDC Conferences

A conference talk exploring continual learning through a real-world MRI diagnostics case study focused on detecting focal cortical dysplasias (FCDs), a common cause of drug-resistant epilepsy. The speaker walks through building a U-Net based model, the challenges of small and imbalanced medical datasets, and how catastrophic forgetting undermines naive fine-tuning. Three families of continual learning methods are explained: regularization-based (elastic weight consolidation), memory replay, and architecture-based approaches. The team applied EWC to adapt their model to new data distributions without retraining from scratch, achieving comparable sensitivity with significantly fewer false positives. The talk concludes with broader applicability of continual learning in production ML systems and highlights Google's new 'nested learning' paradigm as a promising research direction.

Building AI That Learns and Adapts: A Case Study in MRI Diagnostics - Agata Chudzińska