Computational Efficiency and Explanation Quality in Tumor Segmentation Algorithms

This report synthesises findings from 13 peer-reviewed papers addressing the following research question: What is the correlation between computational efficiency (FLOPs, inference time) and explanation quality scores on the human attention benchmark. 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. 6 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: What is the correlation between computational efficiency (FLOPs, inference time) and explanation quality scores on the human attention benchmark?

Autonomous literature synthesis. Automated review score: 8.7/10. Full text and citation available at Assignee Research.