Summary of Assessing Uncertainty Estimation Methods For 3d Image Segmentation Under Distribution Shifts, by Masoumeh Javanbakhat et al.
Assessing Uncertainty Estimation Methods for 3D Image Segmentation under Distribution Shifts
by Masoumeh Javanbakhat, Md Tasnimul Hasan, Cristoph Lippert
First submitted to arxiv on: 10 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 challenge of using machine learning for medical image-based disease detection and diagnosis in practical settings where real-world data may differ significantly from training datasets. To address this limitation, it investigates three uncertainty estimation methods to detect distributionally shifted samples and achieve reliable diagnostic predictions in segmentation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists are working on making sure that deep learning models used for medical imaging can be trusted when they make mistakes or encounter new data. They tested different ways of calculating the uncertainty of these models’ predictions and found that some methods are better than others at identifying when a model is unsure or has seen something new before. This study can help improve the reliability of medical diagnoses made using artificial intelligence. |
Keywords
* Artificial intelligence * Deep learning * Machine learning
