Summary of Improving Segment Anything on the Fly: Auxiliary Online Learning and Adaptive Fusion For Medical Image Segmentation, by Tianyu Huang et al.
Improving Segment Anything on the Fly: Auxiliary Online Learning and Adaptive Fusion for Medical Image Segmentation
by Tianyu Huang, Tao Zhou, Weidi Xie, Shuo Wang, Qi Dou, Yizhe Zhang
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 current variants of the Segment Anything Model (SAM), including the original SAM and Medical SAM, struggle to produce accurate segmentations for medical images. Human experts often correct SAM’s predictions using state-of-the-art annotation tools. To improve this process, we introduce a novel approach that leverages online machine learning to enhance SA during test time. We employ rectified annotations to perform online learning, aiming to boost the segmentation quality of SA on medical images. To improve the effectiveness and efficiency of online learning when integrated with large-scale vision models like SAM, we propose Auxiliary Online Learning (AuxOL). AuxOL creates a small auxiliary model (specialist) in conjunction with SAM (generalist), entails adaptive online-batch and adaptive segmentation fusion. Our experiments on eight datasets covering four medical imaging modalities validate the effectiveness of the proposed method. This work proposes and validates a new, practical, and effective approach for enhancing SA on downstream segmentation tasks, such as medical image segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving the accuracy of computer models that segment (or separate) different parts in medical images. Right now, these models are not very good at this task, so human experts often have to correct their mistakes. The researchers came up with a new way to make these models better by using online learning, which allows them to learn and improve over time. They tested their idea on many different types of medical images and found that it worked really well. This could be an important step forward in helping computers understand and analyze medical images more accurately. |
Keywords
» Artificial intelligence » Image segmentation » Machine learning » Online learning » Sam
