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- W4386647536 abstract "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." @default.
- W4386647536 created "2023-09-13" @default.
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- W4386647536 date "2023-09-10" @default.
- W4386647536 modified "2023-10-18" @default.
- W4386647536 title "Sculpting Efficiency: Pruning Medical Imaging Models for On-Device Inference" @default.
- W4386647536 doi "https://doi.org/10.48550/arxiv.2309.05090" @default.
- W4386647536 hasPublicationYear "2023" @default.
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