Early diagnosis of Parkinson's disease (PD) is crucial for timely treatment and disease management. Recent studies link PD to impaired facial muscle control, manifesting as "masked face" symptoms, offering a novel diagnostic approach through facial expression analysis. However, data privacy concerns and legal restrictions have resulted in significant "data silos", hindering data sharing and limiting the accuracy and generalizability of existing diagnostic models due to small, localized datasets. To address these challenges, we propose an innovative Tri-Factor Adaptive Federated Learning (TriAFL) framework, designed to collaboratively analyze facial expression data across multiple medical institutions while ensuring robust data privacy protection. TriAFL introduces a comprehensive evaluation mechanism that assesses client contributions across three dimensions: gradient, data, and learning efficiency, effectively addressing Non-IID issues arising from data size variations and heterogeneity. To validate the real-world applicability of our method, we collaborate with a hospital to build the largest known facial expression dataset of PD patients. Furthermore, we explore the integration of local data augmentation strategy to further enhance diagnostic accuracy. Comprehensive experimental results demonstrate TriAFL's superior performance over conventional FL methods in classification task, as well as confirms TriAFL's efficacy in PD diagnosis, delivering a rapid, non-invasive screening tool while driving advancements in AI-powered healthcare.

The early diagnosis of Parkinson’s disease (PD) is crucial for potential patients to receive timely treatment and prevent disease progression. Recent studies have shown that PD is closely linked to impairments in facial muscle control, resulting in characteristic “masked face” symptoms. This discovery offers a novel perspective for PD diagnosis by leveraging facial expression recognition and analysis techniques to capture and quantify these features, thereby distinguishing between PD patients and non-PD individuals based on their facial expressions. However, concerns about data privacy and legal restrictions have led to significant “data silos”, posing challenges to data sharing and limiting the accuracy and generalization of existing diagnostic models due to small, localized datasets. To address this issue, we propose an innovative adaptive federated learning approach that aims to jointly analyze facial expression data from multiple medical institutions while preserving data privacy. Our proposed approach comprehensively evaluates each client's contributions in terms of gradient, data, and learning efficiency, overcoming the non-IID issues caused by varying data sizes or heterogeneity across clients. To demonstrate the real-world impact of our approach, we collected a new facial expression dataset of PD patients in collaboration with a hospital. Extensive experiments validate the effectiveness of our proposed method for PD diagnosis and facial expression recognition, offering a promising avenue for rapid, non-invasive initial screening and advancing healthcare intelligence.

Parkinson's disease (PD) is characterized by incurable, rapid progression, and severe disability, severely impacting the lives of patients and their families. With an aging population, the need for early detection of PD is increasing. In vitro diagnosis has attracted attention because of its non-invasiveness and low cost, but there are some problems with the existing methods: 1) facial expression diagnosis has little training data; 2) gait diagnosis requires specialized equipment and acquisition environment, which is poorly generalizable; 3) a single modality is easy to miss the diagnosis; and 4) multimodal diagnostic methods are not universally applicable. To address the above issues, we propose a novel multimodal in vitro diagnostic method for PD based on facial expression and behavioral gait. The method uses a lightweight deep learning model for feature extraction and feature fusion to improve diagnostic accuracy and ease of use. Meanwhile, we have established the largest multimodal PD data set in collaboration with hospitals and conducted a large number of experiments to verify the effectiveness of the method.

This article studies an emerging practical problem called heterogeneous prototype learning (HPL). Unlike the conventional heterogeneous face synthesis (HFS) problem that focuses on precisely translating a face image from a source domain to another target one without removing facial variations, HPL aims at learning the variation-free prototype of an image in the target domain while preserving the identity characteristics. HPL is a compounded problem involving two cross-coupled subproblems, that is, domain transfer and prototype learning (PL), thus making most of the existing HFS methods that simply transfer the domain style of images unsuitable for HPL. To tackle HPL, we advocate disentangling the prototype and domain factors in their respective latent feature spaces and then replacing the source domain with the target one for generating a new heterogeneous prototype. In doing so, the two subproblems in HPL can be solved jointly in a unified manner. Based on this, we propose a disentangled HPL framework, dubbed DisHPL, which is composed of one encoder-decoder generator and two discriminators. The generator and discriminators play adversarial games such that the generator embeds contaminated images into a prototype feature space only capturing identity information and a domain-specific feature space, while generating realistic-looking heterogeneous prototypes. Experiments on various heterogeneous datasets with diverse variations validate the superiority of DisHPL.

It is crucial to promptly diagnose potential Parkinson's disease (PD) patients in order to facilitate early treatment and prevent disease progression. In recent years, there has been growing interest in using facial expressions for in-vitro PD diagnosis due to the distinct "masked face" characteristics of PD patients and the cost-effectiveness of this approach. However, current facial expression-based PD diagnosis methods are hindered by limited training data on PD patients' facial expressions and weak prediction models. To address these issues, we propose a new PD diagnosis method that utilizes facial expression data augmentation and deep neural network prediction. Our approach involves two stages: 1) generating virtual facial expression images depicting six basic emotions (anger, disgust, fear, happiness, sadness, and surprise) through multi-domain adversarial learning to expand the original training data; 2) training a deep neural network prediction model using a combination of the augmented training data from PD patients and facial expression images of normal individuals from public datasets. Qualitative and quantitative experiments confirm the efficacy of our multi-domain adversarial learning-based facial expression synthesis and demonstrate the promising performance of our proposed approach for PD diagnosis.

This paper focuses on a new heterogeneous prototype learning (HPL) problem, which aims at reconstructing the variation-free and identity-preserved prototype in the target domain from a contaminated input image in the source domain. Most existing heterogeneous face synthesis (HFS) methods are unsuitable for HPL, as these methods focus on performing accurate image-to-image translation with facial details unaltered, but cannot effectively remove the input facial variations. In this paper, we propose an identity-aware cycle-consistent network, dubbed IAC2N, for image-to-prototype transformation across domains. To address HPL, IAC2N designs three effective losses, i.e., prototype adversarial loss, label information guided identity loss, and prototype learning cycle loss, in its objective. The first loss is used for transferring the domain style as well as removing the universal facial variations. The latter two losses are used for maintaining the identity consistency during HPL from an explicit and an implicit perspectives, respectively. Furthermore, IAC2N is a joint learning framework that is able to learn the identity feature for the contaminated image via its encoder-decoder structural generator in order to perform heterogeneous face recognition (HFR). Extensive experiments on various heterogeneous face datasets demonstrate the effectiveness of IAC2N in both tasks of HPL and HFR.

Parkinson's disease (PD) is a neurodegenerative disease which is prevalent among the elder population and severely affects the life quality of patients and their families. Therefore, it is important to conduct an early diagnosis for potential patients with PD, so as to promote prompt treatment and avoid the aggravation of the disease. Recently, the in-vitro PD diagnosis based on facial expressions has received increasing attention because of its distinguishability (i.e., PD patients always possess the characteristics of “masked face”) and affordability. However, the performance of the existing facial expression-based PD diagnosis approaches is limited by: 1) the small-scale training data on PD patients' facial expressions, and 2) the weak prediction model. To address these two problems, we propose a new facial expression guided PD diagnosis method based on high-quality training data augmentation and deep neural network prediction. Specifically, the proposed method consists of three stages: Firstly, we synthesize virtual facial expression images with 6 basic emotions (i.e., anger, disgust, fear, happiness, sadness, and surprise) based on multi-domain adversarial learning to approximate the premorbid expressions of PD patients. Secondly, we introduce three facial image quality assessment (FIQA) criteria to measure the quality of these synthesized facial expression images and design a fusion screening strategy that shortlists the high-quality ones to augment the training data. Finally, we train a deep neural network prediction model based on the original and synthesized high-quality facial expression images for PD diagnosis. To show real-world impacts and evaluate the proposed method under different facial expressions, we also create a (currently largest) multiple facial expressions-based PD face dataset in collaboration with a hospital. Extensive experiments are performed to demonstrate the effectiveness of the multi-domain adversarial learning-based facial expression synthesis and the fusion screening strategy, particularly the superior performance of the proposed method for PD diagnosis.