Summary of Average Calibration Error: a Differentiable Loss For Improved Reliability in Image Segmentation, by Theodore Barfoot and Luis Garcia-peraza-herrera and Ben Glocker and Tom Vercauteren
Average Calibration Error: A Differentiable Loss for Improved Reliability in Image Segmentation
by Theodore Barfoot, Luis Garcia-Peraza-Herrera, Ben Glocker, Tom Vercauteren
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This paper addresses the issue of overconfident results in deep neural networks for medical image segmentation. The authors propose using a novel auxiliary loss function, marginal L1 average calibration error (mL1-ACE), to improve pixel-wise calibration without sacrificing segmentation quality. The mL1-ACE loss is directly differentiable, unlike previous methods that required approximate or soft binning approaches. The paper also introduces dataset reliability histograms for visual assessment of calibration in semantic segmentation. Experimental results show a significant reduction in average and maximum calibration error (45% and 55%, respectively) while maintaining a high Dice score (87%) on the BraTS 2021 dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps medical image analysis by fixing a problem with deep neural networks that makes them too confident. The authors created a new way to make these networks more accurate without sacrificing their ability to correctly identify important features. They also developed a tool to visualize how well the networks are performing. The results show that this approach can greatly improve the accuracy of medical image analysis while still maintaining its ability to correctly identify important features. |
Keywords
* Artificial intelligence * Image segmentation * Loss function * Semantic segmentation
