A CNN-based workflow to detect arteriovenous malformations on brain MRI with reproducible training and evaluation.
Clinical imaging datasets are often limited and noisy, requiring robust modeling strategies for dependable detection tasks.
The pipeline provides an interpretable prototype foundation for future clinical decision-support iterations.
Key outcome
A robust ML baseline with documented evaluation criteria for AVM detection scenarios.