Efficient Graph Neural Architecture Search for Medical Imaging in Real-World Clinical Settings

Deploying deep learning in clinical settings requires balancing accuracy with limited computational resources. This is especially challenging in multitask medical imaging, where shared encoders reduce redundancy but task-specific heads remain memory-intensive. We propose Efficient Graph Neural Architecture Search (EGNAS), a gradient-based method that explores a graph-structured space to find compact, task-specific predictors. EGNAS jointly optimizes accuracy and model size using a Pareto-efficient strategy. Evaluated on six MedNIST tasks, it reduces head size by 2.1x on average without performance loss. We further validate EGNAS in a real-world deployment on a low-resource clinical laptop in Algeria, demonstrating its practical utility for resource-constrained healthcare.