用可解释的可训练滤波器自动迁移X光片风格，提升诊断一致性。
An Interpretable X-ray Style Transfer via Trainable Local Laplacian Filter
- 引入可训练局部拉普拉斯滤波器，实现风格迁移的可解释性。
- 新方法使乳腺X光图像风格迁移后结构相似度达0.94，优于基线0.82。
- 适合医学影像分析、放射科医生辅助诊断等场景使用。
放射科医生常偏好特定视觉风格的X光图像以辅助诊断。本文提出一种自动且可解释的X光片风格迁移方法，通过引入可训练的局部拉普拉斯滤波器（LLF），从优化后的重映射函数形状中推断风格特征，增强算法可靠性。为捕捉复杂风格特征，将重映射函数替换为多层感知机（MLP），并添加可训练归一化层。实验表明，该方法能将原始乳腺钼靶图像转换为目标风格，结构相似度（SSIM）达0.94，显著优于基线方法（Aubry et al.）的0.82。
原文摘要 · Abstract (English)
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.
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