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成为广泛研究领域，这是由于来自不同来源、面向各类任务的数据集日益增多。这一问题在处理医疗数据时尤为突出：缺乏方法论标准导致不同影像中心或使用不同设备及协变量采集的数据存在巨大差异。为克服这些局限，我们提出一种新颖、可泛化、且与数据和任务无关的框架，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，支持实数、四元数或超复数模型，并将其泛化应用于单通道数据。QUAVE首先通过四元数小波变换提取不同子带，获得低频/近似子带与高频/细粒度特征。随后，它筛选最具代表性的子带作为输入，替代标准数据样本，接入任何其他神经图像处理模型。我们开展了包含不同数据集、多项图像分析与合成任务（包括重建、分割及模态转换）的广泛实验评估，同时测试了QUAVE与实数模型及四元数模型的结合效果。结果表明该框架在提升网络性能的同时，具备灵活适配多场景、鲁棒应对域漂移的泛化能力。完整代码发布于：https://github.com/ispamm/QWT。