Summary of Segicl: a Multimodal In-context Learning Framework For Enhanced Segmentation in Medical Imaging, by Lingdong Shen et al.
SegICL: A Multimodal In-context Learning Framework for Enhanced Segmentation in Medical Imaging
by Lingdong Shen, Fangxin Shang, Xiaoshuang Huang, Yehui Yang, Haifeng Huang, Shiming Xiang
First submitted to arxiv on: 25 Mar 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 research introduces SegICL, a novel approach for medical image segmentation that leverages In-Context Learning (ICL) to generalize across diverse modalities without requiring fine-tuning or retraining. The model employs text-guided segmentation and in-context learning with a small set of image-mask pairs, allowing it to address new segmentation tasks based on contextual information. Compared to existing methods, SegICL demonstrates improved performance on out-of-distribution (OOD) tasks, with a 1.5-fold increase in segmentation accuracy when provided three shots compared to zero-shot settings. The proposed approach also exhibits comparable performance to mainstream models on OOD and in-distribution tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical image segmentation is a crucial task that involves separating different parts of the body from medical images. Researchers have developed models that can do this job, but they often don’t work well when the images are very different from what they were trained on. A new approach called SegICL tries to solve this problem by using contextual information to help the model make better predictions. It works by giving the model a few examples of what it should be looking for in the image and then letting it learn from those examples. This helps the model generalize better across different types of images and makes it more useful for real-world applications. |
Keywords
» Artificial intelligence » Fine tuning » Image segmentation » Mask » Zero shot
