Summary of Non-negative Subspace Feature Representation For Few-shot Learning in Medical Imaging, by Keqiang Fan et al.
Non-negative Subspace Feature Representation for Few-shot Learning in Medical Imaging
by Keqiang Fan, Xiaohao Cai, Mahesan Niranjan
First submitted to arxiv on: 3 Apr 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 A novel study explores the application of data-based few-shot learning in medical image classification, addressing the scarcity of data in this domain. Researchers investigate various representations of data attributes in a low-dimensional space using non-negative matrix factorization (NMF) and compare it to principal component analysis (PCA). The study uses 14 different datasets covering 11 distinct illness categories, demonstrating that NMF is a competitive alternative to PCA for few-shot learning in medical imaging. Supervised NMF algorithms show greater effectiveness in detecting lesion areas with limited samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical image interpretations are often hindered by the lack of data. A new study shows how using non-negative matrix factorization (NMF) can help solve this problem. NMF is a way to simplify complex data into smaller, more manageable pieces. The study compares NMF to another method called principal component analysis (PCA). With 14 different datasets and 11 illness categories, the results show that NMF is a good alternative to PCA for medical image classification. This can help doctors better detect certain illnesses with limited samples. |
Keywords
» Artificial intelligence » Few shot » Image classification » Pca » Principal component analysis » Supervised
