Summary of Confidence Intervals Uncovered: Are We Ready For Real-world Medical Imaging Ai?, by Evangelia Christodoulou et al.
Confidence intervals uncovered: Are we ready for real-world medical imaging AI?
by Evangelia Christodoulou, Annika Reinke, Rola Houhou, Piotr Kalinowski, Selen Erkan, Carole H. Sudre, Ninon Burgos, Sofiène Boutaj, Sophie Loizillon, Maëlys Solal, Nicola Rieke, Veronika Cheplygina, Michela Antonelli, Leon D. Mayer, Minu D. Tizabi, M. Jorge Cardoso, Amber Simpson, Paul F. Jäger, Annette Kopp-Schneider, Gaël Varoquaux, Olivier Colliot, Lena Maier-Hein
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The paper explores the importance of reporting performance variability in medical imaging AI applications, specifically in segmentation tasks. The authors argue that mean performance values are often misleading and may not accurately reflect a method’s capabilities. They analyze 221 MICCAI segmentation papers from 2023, finding that most (over 50%) do not report performance variability at all. The authors then propose an approximation to estimate the unreported standard deviation in segmentation papers based on the mean Dice similarity coefficient. This allows for the reconstruction of confidence intervals around a method’s mean performance. The results show that for many papers, the top-ranked method may not be significantly better than the second-ranked method, highlighting the need for more comprehensive reporting of performance variability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical imaging is using AI to transform healthcare! But how do we know which methods are best? The authors say we need to look at how well each method does, and what’s good about one method might not be so special after all. They looked at lots of papers that tried to segment medical images (like finding the boundary of a tumor) and found most didn’t even show how much they varied from each other. The authors came up with a way to guess this variation based on how well each method did overall. When they used this method, they found that sometimes the top-ranked method wasn’t really better than the second-ranked one! This means we need to be more careful when picking which methods to use in hospitals. |
