Summary of Active Label Refinement For Robust Training Of Imbalanced Medical Image Classification Tasks in the Presence Of High Label Noise, by Bidur Khanal et al.
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise
by Bidur Khanal, Tianhong Dai, Binod Bhattarai, Cristian Linte
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 In this paper, the authors investigate the impact of label noise on deep learning-based medical image classification. They propose a two-phase approach that combines Learning with Noisy Labels (LNL) and active learning to improve robustness and iteratively clean noisy labels. The method is demonstrated to be superior to previous approaches in handling class imbalance and avoiding misidentification of minority classes as noisy samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how noise in the labels used to train medical image classification models affects their performance. The authors suggest a new way to improve the accuracy of these models, even when they’re trained on noisy data. This approach not only makes the models more robust but also helps clean up the noisy labels over time. |
Keywords
» Artificial intelligence » Active learning » Deep learning » Image classification
