Summary of Zero-shot Medical Phrase Grounding with Off-the-shelf Diffusion Models, by Konstantinos Vilouras et al.
Zero-Shot Medical Phrase Grounding with Off-the-shelf Diffusion Models
by Konstantinos Vilouras, Pedro Sanchez, Alison Q. O’Neil, Sotirios A. Tsaftaris
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 A machine learning-based approach for localizing pathological regions in medical scans without requiring extensive bounding box annotations is presented. The method leverages a Latent Diffusion Model, which contains cross-attention mechanisms that align visual and textual features, to perform phrase grounding in a zero-shot manner. By selecting and refining features via post-processing without additional learnable parameters, the approach achieves competitive results with state-of-the-art methods on a chest X-ray benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of using medical images is being explored. This method helps find specific areas in a scan that are abnormal. It does this without needing to draw boxes around those areas first. Instead, it uses information from text reports about the image to help make the prediction. The model used is called Latent Diffusion Model and it’s good at understanding how words relate to pictures. This method works well even when it doesn’t have any training data for that specific task. It can find abnormalities in chest X-ray images just as well as other methods. |
Keywords
» Artificial intelligence » Bounding box » Cross attention » Diffusion model » Grounding » Machine learning » Zero shot
