Adaptive wavelet filters as practical texture amplifiers for early Parkinson’s disease screening from retinal pathology perspective

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder globally. The eye’s retina is an extension of the brain, and clinical evidence has suggested the great potential of retinal pathology as surrogate biomarkers for early PD diagnosis. In particular, recent studies have shown that texture features extracted from retinal layers based on optical coherence tomography (OCT) images are strongly associated with PD-related retinal pathology. Additionally, frequency domain learning techniques can improve the representational capabilities of deep neural networks (DNNs) by decomposing frequency components that involve rich texture features, which remain underexplored for automated early PD diagnosis in OCT. To bridge this gap, we propose an Adaptive Wavelet Filter (AWF) that serves as the Practical Texture Amplifier, which fully leverages the merits of texture features from the retinal pathology view. Specifically, AWF first enhances feature map diversities and refines feature representations via channel mixer, then emphasizes informative texture feature representations with the well-designed adaptive wavelet filtering token mixer with the aid of frequency domain learning. By embedding AWFs into the DNN stem, AWFNet is constructed for automated early PD screening from OCT images. Additionally, we introduce a novel Balanced Confidence (BC) loss to boost early PD screening performance and trustworthiness of AWFNet, by mining the potential of sample-wise predicted probabilities across all classes and class frequency prior. The extensive experiments manifest the superiority of AWFNet with BC over state-of-the-art methods in terms of early PD screening performance and trustworthiness.