Summary of Patchalign:fair and Accurate Skin Disease Image Classification by Alignment with Clinical Labels, By Aayushman et al.
PatchAlign:Fair and Accurate Skin Disease Image Classification by Alignment with Clinical Labels
by Aayushman, Hemanth Gaddey, Vidhi Mittal, Manisha Chawla, Gagan Raj Gupta
First submitted to arxiv on: 8 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper introduces PatchAlign, a novel approach to enhance skin condition image classification accuracy and fairness by aligning with clinical text representations of skin conditions. By utilizing Graph Optimal Transport (GOT) Loss as a regularizer, the model achieves robust and generalizable representations that perform well across different skin tones even with limited training data. Additionally, the paper proposes a learnable Masked Graph Optimal Transport to further improve fairness metrics by reducing the effect of noise and artifacts in clinical dermatology images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to solve a problem with deep learning models used for automating skin lesion diagnosis. These models are not equally good at diagnosing skin lesions for people from different ethnic groups. The authors propose a new way, called PatchAlign, that makes these models more accurate and fair. It uses something called Graph Optimal Transport Loss to match the images of skin conditions with text descriptions of those conditions. This helps the model work better across different skin tones and even when it has limited training data. |
Keywords
» Artificial intelligence » Deep learning » Image classification
