GROUSE. A task and model agnostic wavelet-driven framework for medical imaging

In recent years, deep learning has permeated the field of medical image analysis gaining increasing attention from clinicians. However, medical images always require specific preprocessing that often includes downscaling due to computational constraints. This may cause a crucial loss of information magnified by the fact that the region of interest is usually a tiny portion of the image. To overcome these limitations, we propose GROUSE, a novel and generalizable framework that produces salient features from medical images by grouping and selecting frequency sub-bands that provide approximations and fine-grained details useful for building a more complete input representation. The framework provides the most enlightening set of bands by learning their statistical dependency to avoid redundancy and by scoring their informativeness to provide meaningful data. This set of representative features can be fed as input to any neural model, replacing the conventional image input. Our method is task- and model-agnostic, thus it can be generalized to any medical image benchmark, as we extensively demonstrate with different tasks, datasets, and model domains. We show that the proposed framework enhances model performance in every test we conduct without requiring ad-hoc preprocessing or network adjustments.