An Interpretable X-ray Style Transfer via Trainable Local Laplacian Filter

TL;DR

This paper introduces an interpretable, trainable X-ray style transfer method using a modified Local Laplacian Filter with neural network components, improving style matching in mammograms with high SSIM scores.

Contribution

It presents a novel trainable and interpretable style transfer approach for X-ray images, enhancing the original Local Laplacian Filter with neural networks for complex style features.

Findings

Achieved SSIM of 0.94 on mammogram style transfer

Enhanced interpretability through remap function analysis

Outperformed baseline LLF method in style matching

Abstract

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.