Summary of Integrating Deep Metric Learning with Coreset For Active Learning in 3d Segmentation, by Arvind Murari Vepa et al.
Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation
by Arvind Murari Vepa, Zukang Yang, Andrew Choi, Jungseock Joo, Fabien Scalzo, Yizhou Sun
First submitted to arxiv on: 24 Nov 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 The proposed method introduces a novel metric learning approach for Coreset-based slice-active learning in 3D medical segmentation, fusing contrastive learning with inherent data groupings in medical imaging. This allows the model to emphasize relevant differences in samples, leading to superior performance on both weak and full annotations across four datasets (medical and non-medical). The approach surpasses existing active learning techniques and obtains better results with low-annotation budgets, which is crucial in medical imaging. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make it easier to train computers to recognize important parts in medical images. Normally, this requires a lot of labeled data from experts, but the new method uses only some of the data to help the computer learn. This can save a lot of time and money. The approach works better than other methods and is especially helpful when there isn’t much annotated data available. |
Keywords
» Artificial intelligence » Active learning
