Summary of An Interpretable X-ray Style Transfer Via Trainable Local Laplacian Filter, by Dominik Eckert et al.
An Interpretable X-ray Style Transfer via Trainable Local Laplacian Filter
by Dominik Eckert, Ludwig Ritschl, Christopher Syben, Christian Hümmer, Julia Wicklein, Marcel Beister, Steffen Kappler, Sebastian Stober
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper presents an automatic and interpretable X-ray image style transfer algorithm, building upon the Local Laplacian Filter (LLF). The proposed approach introduces a trainable remap function, enabling reliability assessments based on the optimized shape. Furthermore, the authors replace the remap function with a Multi-Layer Perceptron (MLP) and add a trainable normalization layer to capture complex X-ray style features. To evaluate its effectiveness, the algorithm transforms unprocessed mammographic images into matching target styles, achieving an SSIM score of 0.94 compared to 0.82 using Aubry et al.’s baseline LLF method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to change X-ray images to match what radiologists prefer. It uses a technique called Local Laplacian Filter (LLF) and makes it trainable, allowing us to understand how the changes are made. This helps ensure that the algorithm works well. The authors also add another layer to capture more details in the images. They test this new approach by changing unprocessed mammogram images into ones that match what radiologists like, achieving a high score of 0.94. |
Keywords
» Artificial intelligence » Style transfer
