Summary of Equitable Skin Disease Prediction Using Transfer Learning and Domain Adaptation, by Sajib Acharjee Dip et al.
Equitable Skin Disease Prediction Using Transfer Learning and Domain Adaptation
by Sajib Acharjee Dip, Kazi Hasan Ibn Arif, Uddip Acharjee Shuvo, Ishtiaque Ahmed Khan, Na Meng
First submitted to arxiv on: 1 Sep 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 This paper addresses the challenge of developing artificial intelligence (AI) diagnostic tools for skin conditions that accurately diagnose diseases across diverse skin tones. Existing AI models face challenges in diagnosing skin ailments on darker skin, highlighting a notable performance gap. The authors employ a transfer-learning approach by integrating multiple pre-trained models from various image domains to improve robustness and inclusiveness of skin condition predictions. The study rigorously evaluates the effectiveness using the Diverse Dermatology Images (DDI) dataset, which includes both underrepresented and common skin tones. Among all methods, Med-ViT emerged as the top performer due to its comprehensive feature representation learned from diverse image sources. To further enhance performance, domain adaptation was conducted using additional skin image datasets like HAM10000. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making computers better at diagnosing skin conditions. Right now, doctors have to look closely at skin images to figure out what’s wrong, but it can be hard for them. The computer models we use today don’t do very well on darker skin tones. To fix this, the authors came up with a new way to teach computers by combining information from many different types of images. They tested this approach using special pictures of skin and found that one type of model was really good at predicting what’s wrong. By adapting this model to more skin image datasets, it got even better! |
Keywords
» Artificial intelligence » Domain adaptation » Transfer learning » Vit
