Summary of Hyden: Hyperbolic Density Representations For Medical Images and Reports, by Zhi Qiao et al.
HYDEN: Hyperbolic Density Representations for Medical Images and Reports
by Zhi Qiao, Linbin Han, Xiantong Zhen, Jia-Hong Gao, Zhen Qian
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 HYDEN (Hyperbolic Density Embedding) approach leverages hyperbolic space for visual semantic representation learning, addressing the issue of semantic uncertainty in medical domain data. By integrating text-aware local features with global image features, HYDEN maps image-text features to density features in hyperbolic space using pseudo-Gaussian distributions. The approach employs an encapsulation loss function to model partial order relations between image-text density distributions. Experimental results demonstrate HYDEN’s interpretability and superior performance compared to baseline methods across zero-shot tasks and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re a doctor trying to find the right medical images from a large database. But, there are many different ways to describe an image, and it can be hard to match the right text description with the correct image. The HYDEN approach helps solve this problem by using special mathematical spaces called hyperbolic spaces. It combines information from both images and text to create a new representation that’s more accurate and easier to understand. |
Keywords
» Artificial intelligence » Embedding » Loss function » Representation learning » Zero shot
