Summary of Boosting Medical Image Classification with Segmentation Foundation Model, by Pengfei Gu and Zihan Zhao and Hongxiao Wang and Yaopeng Peng and Yizhe Zhang and Nishchal Sapkota and Chaoli Wang and Danny Z. Chen
Boosting Medical Image Classification with Segmentation Foundation Model
by Pengfei Gu, Zihan Zhao, Hongxiao Wang, Yaopeng Peng, Yizhe Zhang, Nishchal Sapkota, Chaoli Wang, Danny Z. Chen
First submitted to arxiv on: 16 Jun 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 The Segment Anything Model (SAM) has demonstrated remarkable capabilities in zero-shot segmentation for natural images, particularly in medical image segmentation applications. However, there remains a significant gap in leveraging SAM’s power for medical image classification tasks. This paper fills this knowledge gap by introducing SAMAug-C, an innovative augmentation method that generates variants of original images to enhance classification datasets. The proposed framework processes both raw and augmented image inputs simultaneously, capitalizing on the complementary information offered by both. Experimental results on three public datasets validate the effectiveness of this new approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Segment Anything Model (SAM) is a powerful tool for medical image analysis. In this paper, researchers show how to use SAM to improve classification tasks. They create a new way to generate extra images that help train better models. This method works by creating variations of original images. The scientists also propose a new framework that combines raw and augmented images to get even better results. They test their approach on three public datasets and find it works well. |
Keywords
» Artificial intelligence » Classification » Image classification » Image segmentation » Sam » Zero shot
