Summary of Transfair: Transferring Fairness From Ocular Disease Classification to Progression Prediction, by Leila Gheisi et al.
TransFair: Transferring Fairness from Ocular Disease Classification to Progression Prediction
by Leila Gheisi, Henry Chu, Raju Gottumukkala, Yan Luo, Xingquan Zhu, Mengyu Wang, Min Shi
First submitted to arxiv on: 24 Nov 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 The proposed TransFair model aims to enhance demographic fairness in predicting the progression of ocular diseases, leveraging a fairness-aware attention mechanism and knowledge distillation techniques. By training a fair EfficientNet (FairEN) for disease classification and adapting it for progression prediction, the authors demonstrate effective enhancement of equity in predicting ocular disease progression using both 2D and 3D retinal images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI can improve healthcare services by reducing costs and increasing accessibility, but fairness concerns arise when AI disproportionately affects certain groups. Recent methods have shown promise in addressing group performance disparities, but limited longitudinal data with diverse demographics hinders robust and equitable prediction models. The TransFair model aims to address this issue by transferring a fairness-enhanced disease classification model to the task of progression prediction while preserving fairness. |
Keywords
* Artificial intelligence * Attention * Classification * Knowledge distillation
