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

Dominik Eckert, Ludwig Ritschl, Christopher Syben, Christian Hümmer, Julia Wicklein, Marcel Beister, Steffen Kappler, Sebastian Stober

TL;DR

This work tackles automatic, interpretable X-ray style transfer by introducing a trainable Local Laplacian Filter (LLF). The method replaces the fixed, three-parameter remap with a learnable multimodal mapping $m(\cdot)$ and adds a trainable normalization to better match target styles while preserving interpretability through monotonicity of the remap. On mammographic data, the trainable LLF achieves a peak $SSIM$ of $0.944$ and a low $MSE$ around $0.0105$, outperforming the baseline $LLF$ approach ($SSIM \approx 0.817$, $MSE \approx 0.0738$). The approach remains differentiable and interpretable, enabling integration as an operator in neural networks and offering potential extensions to other imaging modalities with style-aware metrics.

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.

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

TL;DR

This work tackles automatic, interpretable X-ray style transfer by introducing a trainable Local Laplacian Filter (LLF). The method replaces the fixed, three-parameter remap with a learnable multimodal mapping and adds a trainable normalization to better match target styles while preserving interpretability through monotonicity of the remap. On mammographic data, the trainable LLF achieves a peak of and a low around , outperforming the baseline approach (, ). The approach remains differentiable and interpretable, enabling integration as an operator in neural networks and offering potential extensions to other imaging modalities with style-aware metrics.

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.

Paper Structure

This paper contains 12 sections, 4 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Method
  3. llf Algorithm
  4. Trainable llf
  5. Enhanced Remap Function
  6. Normalization Layer
  7. Datasets
  8. Optimization
  9. Experiments & Results
  10. Conclusion
  11. Compliance with ethical standards
  12. Disclaimer

Figures (4)

  • Figure 1: Optimization of llf and a norm. Either an mlp or the rm of paris2011local is employed.
  • Figure 2: This figure illustrates how $\alpha$ and $\beta$ affect the remapping function: $\beta$ influences the slope of the dark blue segment, while $\alpha$ affects the S-shape of the light blue segment.
  • Figure 3: Comparison of llf-transformed images against the target image. Additional examples are provided in the Supplementary Material on https://arxiv.org/abs/your_link_here.
  • Figure 4: Optimized rm of the trainable llf and the baseline $\nabla$-H.