Summary of Adaptive Interactive Segmentation For Multimodal Medical Imaging Via Selection Engine, by Zhi Li et al.
Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine
by Zhi Li, Kai Zhao, Yaqi Wang, Shuai Wang
First submitted to arxiv on: 29 Nov 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 The proposed Strategy-driven Interactive Segmentation Model (SISeg) addresses challenges in medical image analysis by enhancing segmentation performance across various imaging modalities through a selection engine integrated with SAM2. The SISeg model demonstrates robust adaptability and generalization in multi-modal tasks, showcasing improved accuracy and efficiency in automated diagnosis and treatment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The SISeg model is designed to tackle the challenges of medical image analysis by improving segmentation performance across different imaging modalities. This is achieved through a selection engine that integrates with SAM2, allowing for more accurate and efficient segmentation. The model also includes an Adaptive Frame Selection Engine (AFSE) that automatically selects the most relevant frames in 2D image sequences without requiring extensive prior knowledge. |
Keywords
» Artificial intelligence » Generalization » Multi modal
