Summary of Leveraging Ai Predicted and Expert Revised Annotations in Interactive Segmentation: Continual Tuning or Full Training?, by Tiezheng Zhang et al.
Leveraging AI Predicted and Expert Revised Annotations in Interactive Segmentation: Continual Tuning or Full Training?
by Tiezheng Zhang, Xiaoxi Chen, Chongyu Qu, Alan Yuille, Zongwei Zhou
First submitted to arxiv on: 29 Feb 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 Continual Tuning, an innovative approach to interactive segmentation, optimizes the accuracy and efficiency of curating large-scale, detailed-annotated datasets in healthcare. By leveraging AI algorithms and human expertise, this process iteratively improves annotation quality through a collaborative effort between AI predictions and expert revisions. The key challenge lies in effectively utilizing AI predicted and expert revised annotations to enhance AI performance. To address these challenges, the proposed Continual Tuning method tackles two critical issues: catastrophic forgetting and computational inefficiency. It achieves this by designing a shared network for all classes, freezing previously learned classes, and reusing important data with previous annotations. The importance score is calculated based on uncertainty and consistency of AI predictions. Experimental results demonstrate that Continual Tuning outperforms traditional methods, achieving a speed 16x greater without compromising performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine combining artificial intelligence (AI) with human expertise to improve the accuracy and efficiency of curating large datasets in healthcare. This process is called interactive segmentation. It’s like a game where AI makes predictions, humans review them, and then AI improves its predictions based on what humans learned from reviewing. This back-and-forth process keeps improving until it’s almost perfect. The big challenge is figuring out how to use both AI predictions and human revisions to make the AI better. Two main problems arise: forgetting old information and wasting computer power. To solve these issues, a new method called Continual Tuning was developed. It works by having one part of the AI learn from all classes, while another part only learns from changes made by humans. This approach also reuses important data to avoid unnecessary work. The results show that this method is much faster and just as accurate as traditional methods. |
