Summary of Pam: a Propagation-based Model For Segmenting Any 3d Objects Across Multi-modal Medical Images, by Zifan Chen et al.
PAM: A Propagation-Based Model for Segmenting Any 3D Objects across Multi-Modal Medical Images
by Zifan Chen, Xinyu Nan, Jiazheng Li, Jie Zhao, Haifeng Li, Ziling Lin, Haoshen Li, Heyun Chen, Yiting Liu, Lei Tang, Li Zhang, Bin Dong
First submitted to arxiv on: 25 Aug 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 This study presents PAM (Propagating Anything Model), a novel approach to volumetric segmentation in medical imaging. Current methods are limited by requiring manual annotations, being task-specific, or poorly performing with unique features of medical images. PAM leverages a 2D prompt to create a complete 3D segmentation, combining CNN-based UNet processing and Transformer-based attention modules for information propagation between slices. This design enables better generalizability across various imaging modalities. PAM outperforms existing models like MedSAM and SegVol, achieving an average improvement of over 18.1% in dice similarity coefficient (DSC) across 44 medical datasets. It also exhibits stable performance despite prompt deviations and different propagation setups, with faster inference speeds compared to other models. PAM’s one-view prompt design reduces interaction time by about 63.6% compared to two-view prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about a new way to look at medical images. Right now, it takes a lot of work and specific training for computers to understand these images. The researchers created a new method called PAM that can automatically identify different parts of the body in 3D medical images just by looking at one picture. This helps doctors analyze medical images more quickly and accurately. PAM is special because it can handle different types of medical images and even recognize new things it hasn’t seen before. It’s a big improvement over current methods and has many potential uses in hospitals. |
Keywords
» Artificial intelligence » Attention » Cnn » Inference » Prompt » Transformer » Unet
