Summary of Data Alignment For Zero-shot Concept Generation in Dermatology Ai, by Soham Gadgil et al.
Data Alignment for Zero-Shot Concept Generation in Dermatology AI
by Soham Gadgil, Mahtab Bigverdi
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 explores the limitations of training trustworthy classifiers in dermatology due to the scarcity of data with ground-truth concept-level labels. To alleviate this challenge, researchers propose using foundation models like CLIP, which can leverage vast amounts of image-caption pairs on the internet. By fine-tuning CLIP with domain-specific image-caption pairs, classification performance can be improved. However, CLIP’s pre-training data is not well-aligned with medical jargon used by clinicians for diagnoses. To address this issue, the paper develops large language models (LLMs) to generate rich text captions that align with both clinical lexicon and natural human language used in CLIP’s pre-training data. The study uses captions from PubMed articles and extends them through an LLM fine-tuned on several dermatology textbooks. Results show that using captions generated by an expressive, fine-tuned LLM like GPT-3.5 improves zero-shot concept classification performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving AI in dermatology, which is hard because we don’t have enough data with labels that explain what the pictures are of. The researchers want to find a way around this problem using a special kind of AI model called CLIP. CLIP can look at lots of pictures and captions on the internet and learn from them. But, these captions aren’t written in medical language, which is used by doctors to diagnose patients. To fix this, the paper uses another type of AI model called LLMs to generate new captions that match both medical jargon and regular language. The study starts with captions from scientific articles and makes them more detailed using a special textbook-trained LLM. This helps improve AI’s ability to classify pictures without needing labels. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Gpt » Zero shot
