Summary of Anytime, Anywhere, Anyone: Investigating the Feasibility Of Segment Anything Model For Crowd-sourcing Medical Image Annotations, by Pranav Kulkarni et al.
Anytime, Anywhere, Anyone: Investigating the Feasibility of Segment Anything Model for Crowd-Sourcing Medical Image Annotations
by Pranav Kulkarni, Adway Kanhere, Dharmam Savani, Andrew Chan, Devina Chatterjee, Paul H. Yi, Vishwa S. Parekh
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper explores the potential of foundation models like Segment Anything Model (SAM) for crowd-sourcing sparse annotations from non-experts to generate dense segmentation masks for training 3D neural network-based (nnU-Net) models. Recent advances in deep learning have led to exceptional zero-shot generalizability across various domains, including medical imaging. However, these models are often narrowly focused and require domain expertise, making them less translational. The authors evaluate SAM’s ability to generate annotations for training 3D nnU-Net models and find that while SAM-generated annotations exhibit high mean Dice scores compared to ground-truth annotations, nnU-Net models trained on SAM-generated annotations perform significantly worse than those trained on ground-truth annotations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can use a special kind of AI model called Segment Anything Model (SAM) to help people without medical expertise create accurate maps of images. These images are often used to train machines to segment and understand medical data. SAM has shown it can work well with different kinds of images, even ones it’s never seen before. The researchers wanted to see if SAM could be used to get non-experts to label these images so that machines can learn from them. They found that while SAM did a good job at labeling the images, the resulting maps weren’t as accurate as those made by experts. |
Keywords
* Artificial intelligence * Deep learning * Neural network * Sam * Zero shot
