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

• URL: http://arxiv.org/abs/2309.05090v2
• Date: Thu, 2 Nov 2023 00:15:19 GMT
• Title: Sculpting Efficiency: Pruning Medical Imaging Models for On-Device Inference
• Authors: Sudarshan Sreeram and Bernhard Kainz
• Abstract summary: We highlight the excess operational complexity in a suboptimally configured ML model from prior work. Our results show a compression rate of 1148x with minimal loss in quality. We consider avenues for future research in streamlining for clinical researchers to develop models quicker and better suited for real-world use.
• Score: 13.403419873964422
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Leveraging ML advancements to augment healthcare systems can improve patient outcomes. Yet, uninformed engineering decisions in early-stage research inadvertently hinder the feasibility of such solutions for high-throughput, on-device inference, particularly in settings involving legacy hardware and multi-modal gigapixel images. Through a preliminary case study concerning segmentation in cardiology, we highlight the excess operational complexity in a suboptimally configured ML model from prior work and demonstrate that it can be sculpted away using pruning to meet deployment criteria. Our results show a compression rate of 1148x with minimal loss in quality (~4%) and, at higher rates, achieve faster inference on a CPU than the GPU baseline, stressing the need to consider task complexity and architectural details when using off-the-shelf models. With this, we consider avenues for future research in streamlining workflows for clinical researchers to develop models quicker and better suited for real-world use.

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