How does ExpertFlow's predictive expert caching mechanism affect inference latency and memory usage compared t

Abstract

Abstract: In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74\%-85

Research Question

How does ExpertFlow's predictive expert caching mechanism affect inference latency and memory usage compared to static expert allocation baselines on MMBench and SEED-Bench across different input distributions?

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