Summary of Conformal Confidence Sets For Biomedical Image Segmentation, by Samuel Davenport
Conformal confidence sets for biomedical image segmentation
by Samuel Davenport
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- 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 proposes confidence sets for image segmentation models, providing spatial uncertainty guarantees for their output. The approach adapts conformal inference to the imaging setting, using a calibration dataset to obtain thresholds based on the distribution of transformed logit scores within and outside of ground truth masks. The authors prove that these confidence sets contain the true unknown segmented mask with desired probability when applied to new predictions. They also demonstrate the importance of learning score transformations on a learning dataset for optimizing performance. The approach is validated on a polypys tumor dataset, using distance-transformed scores to obtain outer confidence sets and original scores for inner confidence sets, enabling tight bounds on tumor location while controlling false coverage rates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better predictions in image segmentation tasks by providing “confidence sets” that tell us how certain we are about the results. It’s like having a special tool to check if our predictions are correct or not. The authors used a type of math called “conformal inference” to create these confidence sets, which they tested on real images of tumors. They found that this approach can give us accurate and reliable results while also controlling how often we make mistakes. |
Keywords
» Artificial intelligence » Image segmentation » Inference » Mask » Probability
