Summary of How to Build the Best Medical Image Segmentation Algorithm Using Foundation Models: a Comprehensive Empirical Study with Segment Anything Model, by Hanxue Gu and Haoyu Dong and Jichen Yang and Maciej A. Mazurowski
How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model
by Hanxue Gu, Haoyu Dong, Jichen Yang, Maciej A. Mazurowski
First submitted to arxiv on: 15 Apr 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 Automated segmentation is a crucial task in medical image analysis, and deep learning has significantly advanced it. Recently, the Segment Anything Model (SAM) was developed with image segmentation in mind, showing promise similar to foundation models in natural language processing and some vision tasks. However, there are no established guidelines for fine-tuning SAM for optimal performance. This study investigates 18 combinations of backbone architectures, model components, and fine-tuning algorithms across 17 datasets covering various radiology modalities. The findings reveal that (1) fine-tuning SAM leads to better performance than previous methods, (2) parameter-efficient learning in both encoder and decoder is superior, (3) network architecture has a small impact on final performance, and (4) self-supervised learning can improve model performance. Additionally, the study highlights the ineffectiveness of some popular methods and demonstrates few-shot and prompt-based settings. The paper concludes by releasing code and MRI-specific fine-tuned weights, which outperformed the original SAM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical image analysis is important for diagnosing diseases. Researchers have been using artificial intelligence (AI) to help with this task. A new AI model called Segment Anything Model (SAM) has shown promise in segmenting images. However, there was no clear guidance on how to make SAM work best. This study looked at different ways to fine-tune SAM and tested it on many different types of medical images. The results showed that making small changes to the way SAM is trained can make it work even better. The study also found that some methods people have tried in the past don’t actually help, but a few new approaches do. Finally, the researchers shared their code and special weights for MRI images, which worked well. |
Keywords
» Artificial intelligence » Decoder » Deep learning » Encoder » Few shot » Fine tuning » Image segmentation » Natural language processing » Parameter efficient » Prompt » Sam » Self supervised
