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.
Abstract (translated)
放射科医生倾向于选择视觉上更符合其需求的手动调整的X光图像风格，以支持他们的诊断表现。在本研究中，我们提出了一种通过引入可训练版本的局部拉普拉斯滤波器（LLF）来实现自动且可解释的X光风格转换方法。从优化后的重映射函数的形状来看，可以推断出风格转换的特点，并确保算法的可靠性。此外，我们将重映射函数替换为多层感知机（MLP），并添加了一个可训练的归一化层，使LLF能够捕捉复杂的X光图像特征。我们通过将未经处理的乳腺X光图像转化为与目标乳腺图像匹配的风格，并达到与Aubry等人提出的基线LLF风格转换方法相比SSIM值为0.94（而基线方法仅为0.82）的结果，证明了所提出方法的有效性。
URL
https://arxiv.org/abs/2411.07072
