Summary of Addressing Imbalance For Class Incremental Learning in Medical Image Classification, by Xuze Hao et al.
Addressing Imbalance for Class Incremental Learning in Medical Image Classification
by Xuze Hao, Wenqian Ni, Xuhao Jiang, Weimin Tan, Bo Yan
First submitted to arxiv on: 18 Jul 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 The proposed plug-in methods aim to mitigate catastrophic forgetting in class incremental learning (CIL) for medical image classification. A CIL-balanced classification loss is introduced to alleviate classifier bias toward majority classes, while a distribution margin loss enforces intra-class compactness and alleviates inter-class overlap in the embedding space. The effectiveness of these methods is evaluated on three benchmark datasets: CCH5000, HAM10000, and EyePACS, demonstrating improved performance over state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better diagnose and treat diseases by improving how computers learn from medical images. Currently, computers are great at recognizing pictures of different types of cancer, but they get confused when they see new types of cancer that they haven’t learned about before. The researchers developed two simple ways to help computers remember what they’ve learned so far and adapt to new information. They tested these methods on many real-world medical images and showed that they work better than other approaches. |
Keywords
» Artificial intelligence » Classification » Embedding space » Image classification
