AMM-Diff：用于缺失模态补全的自适应多模态扩散网络
AMM-Diff: Adaptive Multi-Modality Diffusion Network for Missing Modality Imputation
AI总结：
针对临床中MR模态缺失影响脑肿瘤分割的问题，提出自适应多模态扩散网络AMM-Diff，通过图像-频率融合网络学习统一表示并结合自适应重建策略，在BraTS 2021上实现任意输入配置下的缺失模态补全。
摘要：
在临床实践中，由于复杂的采集协议、严格的隐私法规或特定的临床需求，完整的影像采集并不总是可行的。然而，缺失的MR模态对脑肿瘤分割等任务构成了重大挑战，尤其是在基于深度学习的分割中，因为每种模态都提供了对提高准确性至关重要的互补信息。一种有前景的解决方案是缺失数据补全，即从可用模态生成缺失模态。虽然生成模型已被广泛用于此目的，但大多数最先进的方法仅限于单一或双重目标转换，缺乏根据不同的输入配置生成缺失模态的适应性。为了解决这一问题，我们提出了一种自适应多模态扩散网络（AMM-Diff），这是一种新颖的基于扩散的生成模型，能够处理任意数量的输入模态并生成缺失的模态。我们设计了一个图像-频率融合网络（IFFN），通过一个自监督预文本任务，在全部输入模态及其选定的高频傅里叶分量上学习统一的特征表示。所提出的扩散模型利用这种表示，封装了完整模态的先验知识，并将其与自适应重建策略相结合，以实现缺失模态的补全。在BraTS 2021数据集上的实验结果证明了我们方法的有效性。
英文摘要：
In clinical practice, full imaging is not always feasible, often due to complex acquisition protocols, stringent privacy regulations, or specific clinical needs. However, missing MR modalities pose significant challenges for tasks like brain tumor segmentation, especially in deep learning-based segmentation, as each modality provides complementary information crucial for improving accuracy. A promising solution is missing data imputation, where absent modalities are generated from available ones. While generative models have been widely used for this purpose, most state-of-the-art approaches are limited to single or dual target translations, lacking the adaptability to generate missing modalities based on varying input configurations. To address this, we propose an Adaptive Multi-Modality Diffusion Network (AMM-Diff), a novel diffusion-based generative model capable of handling any number of input modalities and generating the missing ones. We designed an Image-Frequency Fusion Network (IFFN) that learns a unified feature representation through a self-supervised pretext task across the full input modalities and their selected high-frequency Fourier components. The proposed diffusion model leverages this representation, encapsulating prior knowledge of the complete modalities, and combines it with an adaptive reconstruction strategy to achieve missing modality completion. Experimental results on the BraTS 2021 dataset demonstrate the effectiveness of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
