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.

翻译：由于来自不同来源及各类任务的数据集日益丰富，神经网络泛化能力正成为广泛研究的领域。这一问题在处理医学数据时尤为突出——因缺乏方法学标准，不同影像中心提供的成像数据或采用不同设备及协变量采集的数据间存在巨大差异。为克服这些局限，我们提出了一种新颖、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）可轻松集成到任何已有的医学图像分析或合成任务中，并支持实数域、四元数域或超复数域模型，将其应用推广至单通道数据。QUAVE首先通过四元数小波变换提取不同子带，获得低频/近似子带与高频/细粒度特征，随后筛选最具代表性的子带集合作为其他神经模型的输入以处理图像，替代标准数据样本。我们开展了涵盖不同数据集、多种图像分析与合成任务的广泛实验评估（包括重建、分割及模态转换），并分别在实数域与四元数域模型中验证QUAVE性能。结果表明，该框架在提升网络性能的同时具有灵活适应多场景的能力，充分验证了其有效性与泛化性。