Building AI That Learns and Adapts: A Case Study in MRI Diagnostics - Agata Chudzińska
This technical talk explores Continual Learning (CL), positioning it as a critical missing piece for building robust, adaptive AI systems. Using a case study in MRI diagnostics for detecting Focal Cortical Dysplasias (FCDs) in epilepsy patients, the speaker details how CL techniques mitigate issues like data drift and catastrophic forgetting. The presentation covers advanced ML architectures (Encoder-Decoder), preprocessing steps (e.g., segmentation, Junction/Extension filters), and three main CL methods: regularization-based, memory-based, and architecture-based. The goal is to enable models to adapt continuously in privacy-sensitive environments without requiring full retraining.
Key takeaways
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Continual Learning (CL) addresses core ML deployment challenges.
3:30
CL allows AI models to learn and adapt over time from new data streams without forgetting previously acquired knowledge, solving issues like catastrophic forgetting and data drift. This is crucial for high-stakes fields like medicine.
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Medical imaging requires specialized preprocessing and architectures.
6:50
For FCD detection from MRI scans, the process involves brain tissue segmentation and applying feature enhancement filters (e.g., Junction and Extension) before feeding data into an Encoder-Decoder variant of a neural network architecture.
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CL methods offer solutions for resource constraints.
10:05
Instead of retraining from scratch (which is costly, time-consuming, and raises privacy concerns), CL utilizes regularization-based, memory-based, or architecture-based approaches to maintain performance while adapting to new tasks.
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The future of AI requires continuous adaptation.
20:05
The speaker emphasizes that CL is not just a 'nice to have' feature but a strategic architectural choice for any production-level ML system dealing with evolving data and strict privacy requirements.