Summary of Pm2: a New Prompting Multi-modal Model Paradigm For Few-shot Medical Image Classification, by Zhenwei Wang et al.
PM2: A New Prompting Multi-modal Model Paradigm for Few-shot Medical Image Classification
by Zhenwei Wang, Qiule Sun, Bingbing Zhang, Pengfei Wang, Jianxin Zhang, Qiang Zhang
First submitted to arxiv on: 13 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 proposes a novel framework for few-shot learning in medical image classification, dubbed PM2 (Prompting Multi-Modal Model). The key innovation is the introduction of a text-based “prompt” that supplements visual data, enabling effective classification across diverse modalities. To explore the potential of prompt engineering, five distinct schemes are investigated under the new paradigm. Additionally, the paper explores alternative linear probing methods, leveraging global covariance pooling and efficient matrix power normalization to aggregate visual tokens. The proposed approach is evaluated on three medical datasets, demonstrating significant performance gains over state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help computers learn from very few examples of medical images. Right now, it’s hard for computers to learn from these images because there aren’t many examples to train on. The researchers propose a solution that uses both visual and text-based information to help the computer understand what it’s looking at. They also test different ways to use this approach and show that it works well on three medical image datasets. |
Keywords
* Artificial intelligence * Classification * Few shot * Image classification * Multi modal * Prompt * Prompting
