Summary of Achieving Fairness Through Channel Pruning For Dermatological Disease Diagnosis, by Qingpeng Kong et al.
Achieving Fairness Through Channel Pruning for Dermatological Disease Diagnosis
by Qingpeng Kong, Ching-Hao Chiu, Dewen Zeng, Yu-Jen Chen, Tsung-Yi Ho, Jingtong hu, Yiyu Shi
First submitted to arxiv on: 14 May 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 proposed Soft Nearest Neighbor Loss-based channel pruning framework achieves fairness in medical image classification models by selectively pruning critical channels that contribute differently to the accuracy of different groups. This innovative approach improves fairness without sacrificing accuracy significantly, and is validated on two skin lesion diagnosis datasets across multiple sensitive attributes. The method leverages traditional channel pruning for acceleration, but also demonstrates its potential as a potent tool for achieving fairness in deep learning-based models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In medical imaging, artificial intelligence (AI) models are being developed to diagnose diseases from images. However, these models can be biased towards certain groups of people. To fix this problem, researchers have come up with an idea called “channel pruning”. This method helps make AI models fairer by removing parts that affect different groups differently. The proposed framework uses channel pruning to make sure the model is fair and accurate for everyone. |
Keywords
» Artificial intelligence » Deep learning » Image classification » Nearest neighbor » Pruning
