Summary of Coin: Counterfactual Inpainting For Weakly Supervised Semantic Segmentation For Medical Images, by Dmytro Shvetsov et al.
COIN: Counterfactual inpainting for weakly supervised semantic segmentation for medical images
by Dmytro Shvetsov, Joonas Ariva, Marharyta Domnich, Raul Vicente, Dmytro Fishman
First submitted to arxiv on: 19 Apr 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 In this paper, the authors explore the capabilities of weakly supervised semantic segmentation in medical imaging, specifically in computed tomography (CT) scans. They develop a novel counterfactual inpainting approach (COIN) that generates explanations for deep learning models, enabling precise segmentations of pathologies without relying on pre-existing segmentation masks. The method uses image-level labels, which are easier to acquire than detailed segmentation masks. The authors demonstrate the effectiveness of COIN by segmenting synthetic targets and actual kidney tumors from CT images. They show that COIN surpasses established attribution methods, such as RISE, ScoreCAM, and LayerCAM, making it a promising approach for semantic segmentation in medical imaging. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers are trying to make computers better at looking at medical images like X-rays and CT scans. They want to teach computers to find things that aren’t normal in these pictures, like tumors or broken bones. The problem is that these computers need a lot of examples of what’s normal and what’s not, but it’s hard to get those examples. So, the researchers came up with a new way to make the computer learn without needing all those examples. It works by changing the picture in a way that makes the computer think the abnormal thing isn’t there anymore. They tested this method on real medical images and found that it worked better than other methods they tried. This could be an important step forward in making computers more useful for healthcare, where we don’t have as much data to work with. |
Keywords
» Artificial intelligence » Deep learning » Semantic segmentation » Supervised
