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.
@article{arxiv.2411.07072,
title = {An Interpretable X-ray Style Transfer via Trainable Local Laplacian Filter},
author = {Dominik Eckert and Ludwig Ritschl and Christopher Syben and Christian Hümmer and Julia Wicklein and Marcel Beister and Steffen Kappler and Sebastian Stober},
journal= {arXiv preprint arXiv:2411.07072},
year = {2025}
}