Summary of Backmix: Mitigating Shortcut Learning in Echocardiography with Minimal Supervision, by Kit Mills Bransby et al.
BackMix: Mitigating Shortcut Learning in Echocardiography with Minimal Supervision
by Kit Mills Bransby, Arian Beqiri, Woo-Jin Cho Kim, Jorge Oliveira, Agisilaos Chartsias, Alberto Gomez
First submitted to arxiv on: 27 Jun 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 paper proposes a method to prevent neural networks from learning spurious correlations by focusing on the wrong features. This issue, known as the Clever Hans effect, occurs when models learn to predict outcomes based on background cues rather than relevant image content. The authors introduce BackMix, a random background augmentation technique that samples uncorrelated backgrounds from other examples in the training set. This approach helps models focus on the relevant data and become invariant to external factors. The method is extended to semi-supervised learning settings with as little as 5% labeled data. A loss weighting mechanism, wBackMix, is also proposed to emphasize the contribution of augmented examples. The authors validate their method on both in-distribution and out-of-distribution datasets, demonstrating significant improvements in classification accuracy, region focus, and generalizability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to solve a problem where AI models learn to predict things correctly but for the wrong reasons. This can happen when models use background information instead of looking at what’s really important. The authors created a way to fix this by mixing up the backgrounds in the training data, so the model learns to focus on the right features. They tested their method and it worked well even with only a small amount of labeled data. The goal is to make AI models better at making accurate predictions without relying on shortcuts. |
Keywords
» Artificial intelligence » Classification » Semi supervised
