Summary of Adapting Segment Anything Model to Melanoma Segmentation in Microscopy Slide Images, by Qingyuan Liu and Avideh Zakhor
Adapting Segment Anything Model to Melanoma Segmentation in Microscopy Slide Images
by Qingyuan Liu, Avideh Zakhor
First submitted to arxiv on: 3 Oct 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 novel approach utilizes the Segment Anything Model (SAM) for automatic melanoma segmentation in Whole Slide Images (WSIs). The method employs an initial semantic segmentation model to generate preliminary masks, which are then used to prompt SAM. A dynamic prompting strategy is designed to optimize coverage and quality of prompts on high-resolution slide images. To refine the process, in-situ melanoma detection and low-confidence region filtering are implemented. Segformer is chosen as the initial segmentation model, while EfficientSAM is used for fine-tuning. The results demonstrate that this approach outperforms state-of-the-art methods and baseline Segformer by 9.1% in IoU. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors diagnose skin cancer better. It uses a special computer model to look at pictures of skin cells under a microscope and find the cancerous parts. The model is really good at finding these parts, even when they’re tiny or hard to see. This can help doctors make more accurate diagnoses and treat patients more effectively. |
Keywords
» Artificial intelligence » Fine tuning » Prompt » Prompting » Sam » Semantic segmentation
