Summary of Touchstone Benchmark: Are We on the Right Way For Evaluating Ai Algorithms For Medical Segmentation?, by Pedro R. A. S. Bassi et al.

Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?

by Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang, Fabian Isensee, Zifu Wang, Jieneng Chen, Yu-Cheng Chou, Yannick Kirchhoff, Maximilian Rokuss, Ziyan Huang, Jin Ye, Junjun He, Tassilo Wald, Constantin Ulrich, Michael Baumgartner, Saikat Roy, Klaus H. Maier-Hein, Paul Jaeger, Yiwen Ye, Yutong Xie, Jianpeng Zhang, Ziyang Chen, Yong Xia, Zhaohu Xing, Lei Zhu, Yousef Sadegheih, Afshin Bozorgpour, Pratibha Kumari, Reza Azad, Dorit Merhof, Pengcheng Shi, Ting Ma, Yuxin Du, Fan Bai, Tiejun Huang, Bo Zhao, Haonan Wang, Xiaomeng Li, Hanxue Gu, Haoyu Dong, Jichen Yang, Maciej A. Mazurowski, Saumya Gupta, Linshan Wu, Jiaxin Zhuang, Hao Chen, Holger Roth, Daguang Xu, Matthew B. Blaschko, Sergio Decherchi, Andrea Cavalli, Alan L. Yuille, Zongwei Zhou

First submitted to arxiv on: 6 Nov 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary A novel approach is proposed to tackle the challenges in testing artificial intelligence (AI) performance. The standard benchmarks often have limitations such as small test sets, oversimplified metrics, and unfair comparisons, which can lead to good performance on benchmarks not translating to real-world success. To address these issues, Touchstone, a large-scale collaborative segmentation benchmark of 9 abdominal organ types is introduced. This benchmark uses 5,195 training CT scans from 76 hospitals and 5,903 testing CT scans from 11 additional hospitals, enhancing the statistical significance of results and evaluating AI algorithms across various scenarios. The authors invited developers of 19 AI algorithms to train their models, which were then evaluated by a third-party team on three test sets. Additionally, pre-existing AI frameworks such as MONAI, nnU-Net, and others were also evaluated. This benchmark aims to encourage innovation in AI algorithms for the medical domain.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Artificial intelligence (AI) is becoming more important in our daily lives, but how can we make sure it’s doing a good job? Right now, there are some problems with the way we test AI performance. For example, the tests might be too small or use simple metrics that don’t show the whole picture. This means that just because an AI does well on one test, it doesn’t mean it will do well in real-life situations. To fix this, a new test called Touchstone is being created. It’s a big test that looks at 9 different types of organs and uses CT scans from many hospitals around the world. This makes sure that the results are reliable and can be used to compare different AI algorithms. The goal is to make sure that these algorithms work well in real-life situations, not just on one specific test.