Sculpting Efficiency: Pruning Medical Imaging Models for On-Device Inference

Applying ML advancements to healthcare can improve patient outcomes. However, the sheer operational complexity of ML models, combined with legacy hardware and multi-modal gigapixel images, poses a severe deployment limitation for real-time, on-device inference. We consider filter pruning as a solution, exploring segmentation models in cardiology and ophthalmology. Our preliminary results show a compression rate of up to 1148x with minimal loss in quality, stressing the need to consider task complexity and architectural details when using off-the-shelf models. At high compression rates, filter-pruned models exhibit faster inference on a CPU than the GPU baseline. We also demonstrate that such models' robustness and generalisability characteristics exceed that of the baseline and weight-pruned counterparts. We uncover intriguing questions and take a step towards realising cost-effective disease diagnosis, monitoring, and preventive solutions.