Summary of Stylex: a Trainable Metric For X-ray Style Distances, by Dominik Eckert et al.
StyleX: A Trainable Metric for X-ray Style Distances
by Dominik Eckert, Christopher Syben, Christian Hümmer, Ludwig Ritschl, Steffen Kappler, Sebastian Stober
First submitted to arxiv on: 23 May 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 paper proposes a novel deep learning-based metric for quantifying style differences between X-ray images. The goal is to help radiologists adapt to diverse image styles introduced by advances in X-ray technology. The authors introduce an encoder that generates X-ray image style representations, trained using Simple Siamese learning without explicit knowledge of style distances. During inference, the encoder outputs are used to calculate a distance metric for non-matching image pairs. The proposed method is evaluated through t-SNE analysis and human perception-based assessments, demonstrating its ability to quantify style differences effectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps radiologists work with different X-ray images by creating a way to measure how similar or different they are. It uses a special kind of artificial intelligence called deep learning to make this measurement. The researchers created an “encoder” that can turn X-ray images into styles, and then used this encoder to calculate distances between pairs of images that don’t match. They tested their method using a technique called t-SNE analysis and by asking people to rate how similar the images were. The results show that their method is good at measuring style differences, which could help radiologists choose the best image for diagnosis. |
Keywords
» Artificial intelligence » Deep learning » Encoder » Inference
