Summary of Medical Image Debiasing by Learning Adaptive Agreement From a Biased Council, By Luyang Luo et al.
Medical Image Debiasing by Learning Adaptive Agreement from a Biased Council
by Luyang Luo, Xin Huang, Minghao Wang, Zhuoyue Wan, Hao Chen
First submitted to arxiv on: 22 Jan 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 novel debiasing framework called Adaptive Agreement from a Biased Council (Ada-ABC) to tackle dataset bias in medical images. Ada-ABC develops a biased council of multiple classifiers optimized with generalized cross entropy loss, which learns the dataset bias and guides the training of a debiasing model. The debiasing model is trained to learn adaptive agreement with the biased council by agreeing on correctly predicted samples and disagreeing on wrongly predicted ones. This approach allows the debiasing model to learn target attributes without spurious correlations while leveraging rich information from samples with spurious correlations. The paper theoretically demonstrates that the debiasing model can learn target features when the biased model captures dataset bias, and experimentally shows that Ada-ABC outperforms competitive approaches in mitigating dataset bias for medical image classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making sure that machine learning models are fair and accurate when it comes to medical images. It’s like trying to make a camera take good pictures of people without being biased towards certain groups. The problem is that current methods don’t work well because they’re not designed to handle the biases in medical image datasets. This paper proposes a new method called Ada-ABC, which tries to fix this by training multiple models together and making them agree on what’s important and what’s not. |
Keywords
» Artificial intelligence » Cross entropy » Image classification » Machine learning
