Introduction to SAM-Med2D
Highlights
SAM-Med2D is a remarkable project that stands out for several key reasons:
- It offers the largest dataset ever collected for medical image segmentation, with an impressive 4.6 million images and nearly 20 million masks, making it invaluable for training advanced models.
- The project provides comprehensive fine-tuning based on the Segment Anything Model (SAM), enhancing its applicability and performance.
- SAM-Med2D has been extensively evaluated across large-scale datasets, ensuring its reliability and effectiveness.
Recent Updates
Significant advancements have been made in the SAM-Med2D project:
- The extensive SA-Med2D-20M dataset is now available on the Hugging Face platform as of December 5, 2023.
- A detailed article about this dataset was released on November 21, 2023.
- Additional releases include the SAM-Med3D focused on 3D medical imaging, as well as training and testing codes, pre-trained models, and an online demo.
Dataset
The dataset employed by SAM-Med2D is vast, encompassing 4.6 million images and 19.7 million masks. It covers 10 different medical data types, spanning various anatomical structures, lesions, and 31 primary human organs. This makes it the largest and most diverse dataset available for medical image segmentation.
Framework
SAM-Med2D utilizes a sophisticated framework where the image encoder is kept constant while integrating learnable adapter layers in each Transformer block. This setup captures domain-specific knowledge in the medical field. The prompt encoder is fine-tuned using different types of information to support interactive training and parameter updates.
Results
When tested, the model's performance varied based on the resolution and methodology. The results, in terms of bounding box accuracy, point prompts, and frames per second, show that SAM-Med2D is not only effective but also accessible via different checkpoints.
Visualization
SAM-Med2D includes a visualization feature that allows users to understand the segmentation process and results more intuitively.
Training and Testing
To train and test using SAM-Med2D, users can prepare their own datasets following specific demos provided. Training involves several parameters like image size and mask number, with the option to use mixed-precision. Testing includes batch processing and saving predictions.
Deployment
SAM-Med2D can be exported to ONNX format, allowing for easier deployment using onnxruntime for inference, showcasing its versatility in real-world applications.
Experience SAM-Med2D
SAM-Med2D can be tried through various platforms including:
- Gradio Online Demo: Available on OpenXLab for immediate access.
- Notebook Demo: Users can explore locally with provided examples.
- Gradio Local Deployment: Users can set up and test locally.
Ongoing Development
The project continues to progress with dataset and code releases, pre-trained models, and more enhancements to make SAM-Med2D a leader in medical image segmentation technology.
Discussion and Collaboration
SAM-Med2D warmly welcomes feedback and collaboration, offering a discussion group via WeChat for community engagement. The project also seeks global collaboration and talent to further its mission in medical imaging advancements.
In conclusion, SAM-Med2D is a comprehensive and pioneering project in medical imaging, providing vast resources and tools for researchers and practitioners to explore and innovate in medical image segmentation.