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Leveraging ML advancements to augment healthcare systems can improve patientoutcomes. Yet, uninformed engineering decisions in early-stage researchinadvertently hinder the feasibility of such solutions for high-throughput,on-device inference, particularly in settings involving legacy hardware andmulti-modal gigapixel images. Through a preliminary case study concerningsegmentation in cardiology, we highlight the excess operational complexity in asuboptimally configured ML model from prior work and demonstrate that it can besculpted away using pruning to meet deployment criteria. Our results show acompression rate of 1148x with minimal loss in quality (~4%) and, at higherrates, achieve faster inference on a CPU than the GPU baseline, stressing theneed to consider task complexity and architectural details when usingoff-the-shelf models. With this, we consider avenues for future research instreamlining workflows for clinical researchers to develop models quicker andbetter suited for real-world use.

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