An Interpretable X-ray Style Transfer via Trainable Local Laplacian Filter
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917901348372480 |
|---|---|
| author | Eckert, Dominik Ritschl, Ludwig Syben, Christopher Hümmer, Christian Wicklein, Julia Beister, Marcel Kappler, Steffen Stober, Sebastian |
| author_facet | Eckert, Dominik Ritschl, Ludwig Syben, Christopher Hümmer, Christian Wicklein, Julia Beister, Marcel Kappler, Steffen Stober, Sebastian |
| contents | Radiologists have preferred visual impressions or 'styles' of X-ray images that are manually adjusted to their needs to support their diagnostic performance. In this work, we propose an automatic and interpretable X-ray style transfer by introducing a trainable version of the Local Laplacian Filter (LLF). From the shape of the LLF's optimized remap function, the characteristics of the style transfer can be inferred and reliability of the algorithm can be ensured. Moreover, we enable the LLF to capture complex X-ray style features by replacing the remap function with a Multi-Layer Perceptron (MLP) and adding a trainable normalization layer. We demonstrate the effectiveness of the proposed method by transforming unprocessed mammographic X-ray images into images that match the style of target mammograms and achieve a Structural Similarity Index (SSIM) of 0.94 compared to 0.82 of the baseline LLF style transfer method from Aubry et al. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_07072 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Interpretable X-ray Style Transfer via Trainable Local Laplacian Filter Eckert, Dominik Ritschl, Ludwig Syben, Christopher Hümmer, Christian Wicklein, Julia Beister, Marcel Kappler, Steffen Stober, Sebastian Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Radiologists have preferred visual impressions or 'styles' of X-ray images that are manually adjusted to their needs to support their diagnostic performance. In this work, we propose an automatic and interpretable X-ray style transfer by introducing a trainable version of the Local Laplacian Filter (LLF). From the shape of the LLF's optimized remap function, the characteristics of the style transfer can be inferred and reliability of the algorithm can be ensured. Moreover, we enable the LLF to capture complex X-ray style features by replacing the remap function with a Multi-Layer Perceptron (MLP) and adding a trainable normalization layer. We demonstrate the effectiveness of the proposed method by transforming unprocessed mammographic X-ray images into images that match the style of target mammograms and achieve a Structural Similarity Index (SSIM) of 0.94 compared to 0.82 of the baseline LLF style transfer method from Aubry et al. |
| title | An Interpretable X-ray Style Transfer via Trainable Local Laplacian Filter |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2411.07072 |