Summary of Looking Beyond What You See: An Empirical Analysis on Subgroup Intersectional Fairness For Multi-label Chest X-ray Classification Using Social Determinants Of Racial Health Inequities, by Dana Moukheiber et al.
Looking Beyond What You See: An Empirical Analysis on Subgroup Intersectional Fairness for Multi-label Chest X-ray Classification Using Social Determinants of Racial Health Inequities
by Dana Moukheiber, Saurabh Mahindre, Lama Moukheiber, Mira Moukheiber, Mingchen Gao
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY)
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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 AI research paper proposes a framework for achieving accurate diagnostic outcomes in disease diagnosis using chest X-rays while ensuring fairness across intersectional groups. The study builds upon existing advancements in deep learning models, addressing inherent biases that can lead to disparities in prediction accuracy. A novel approach is presented, which involves retraining the last classification layer of pre-trained models using a balanced dataset across groups. This method also accounts for fairness constraints and integrates class-balanced fine-tuning for multi-label settings. The evaluation on the MIMIC-CXR dataset demonstrates an optimal tradeoff between accuracy and fairness compared to baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers have developed a new way to use deep learning models to diagnose diseases from chest X-rays while making sure it’s fair for everyone, regardless of their social background or other factors. They want to make sure the model is as good as possible at diagnosing diseases, but also that it doesn’t unfairly favor certain groups over others. |
Keywords
* Artificial intelligence * Classification * Deep learning * Fine tuning
