Summary of Medical Image Segmentation with Intent: Integrated Entropy Weighting For Single Image Test-time Adaptation, by Haoyu Dong and Nicholas Konz and Hanxue Gu and Maciej A. Mazurowski
Medical Image Segmentation with InTEnt: Integrated Entropy Weighting for Single Image Test-Time Adaptation
by Haoyu Dong, Nicholas Konz, Hanxue Gu, Maciej A. Mazurowski
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed approach to test-time adaptation (TTA) in medical image segmentation models leverages a single unlabeled test image to adapt the model without requiring multiple test images from the same domain. The existing TTA techniques, which directly minimize the entropy of predictions, are found to be insufficient in this setting due to the unstable batch normalization layer statistics. Instead, the proposed method integrates over predictions made with various estimates of target domain statistics between the training and test statistics, weighted based on their entropy statistics. This approach is validated on 24 source/target domain splits across three medical image datasets, achieving an average improvement of 2.9% Dice coefficient compared to the leading method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical researchers are trying to make medical imaging models work better in new situations. Right now, these models get stuck when they’re used with new images because they don’t have enough information about what those images look like. This problem is especially big for medical imaging, where getting more data can be hard and expensive. The team behind this paper found that old methods for adapting to new images didn’t work very well in this situation. They came up with a new way to make the model work better by using different estimates of what the new image looks like and weighing them based on how sure they are about each one. This method worked really well, beating other approaches by 2.9% on average. |
Keywords
» Artificial intelligence » Batch normalization » Image segmentation
