Neural network generalizability is becoming a broad research field due to the increasing availability of datasets from different sources and for various tasks. This issue is even wider when processing medical data, where a lack of methodological standards causes large variations being provided by different imaging centers or acquired with various devices and cofactors. To overcome these limitations, we introduce a novel, generalizable, data- and task-agnostic framework able to extract salient features from medical images. The proposed quaternion wavelet network (QUAVE) can be easily integrated with any pre-existing medical image analysis or synthesis task, and it can be involved with real, quaternion, or hypercomplex-valued models, generalizing their adoption to single-channel data. QUAVE first extracts different sub-bands through the quaternion wavelet transform, resulting in both low-frequency/approximation bands and high-frequency/fine-grained features. Then, it weighs the most representative set of sub-bands to be involved as input to any other neural model for image processing, replacing standard data samples. We conduct an extensive experimental evaluation comprising different datasets, diverse image analysis, and synthesis tasks including reconstruction, segmentation, and modality translation. We also evaluate QUAVE in combination with both real and quaternion-valued models. Results demonstrate the effectiveness and the generalizability of the proposed framework that improves network performance while being flexible to be adopted in manifold scenarios and robust to domain shifts. The full code is available at: https://github.com/ispamm/QWT.

翻译：神经网络泛化能力正日益成为一个广泛的研究领域，这得益于来自不同来源、面向不同任务的数据集日益增多。在处理医学数据时，这一问题尤为突出，由于缺乏统一的方法学标准，不同影像中心或使用不同设备及协变量采集的数据往往存在巨大差异。为克服这些局限性，我们提出了一种新颖的、可泛化的、数据与任务无关的框架，能够从医学图像中提取显著特征。所提出的四元数小波网络（QUAVE）可轻松集成到任何现有的医学图像分析或合成任务中，并能与实数、四元数或超复数模型结合使用，将其适用范围推广至单通道数据。QUAVE首先通过四元数小波变换提取不同子带，得到低频/近似子带与高频/细粒度特征。随后，它对最具代表性的子带集合进行加权处理，作为输入替代标准数据样本，馈入任何其他用于图像处理的神经模型。我们开展了广泛的实验评估，涵盖不同数据集、多样化的图像分析与合成任务（包括重建、分割和模态转换），并评估了QUAVE与实数及四元数值模型的结合效果。结果表明，所提框架在提升网络性能的同时，具备良好的灵活性与领域适应性，能够在多种场景中稳定应用。完整代码已发布于：https://github.com/ispamm/QWT。