Yan Zhuang

University of Virginia | UVa · Department of Electrical and Computer Engineering (ECE)

Learning from point sets is an essential component in many computer vision and machine learning applications. Native, unordered, and permutation invariant set structure space is challenging to model, particularly for point set classification under spatial deformations. Here we propose a framework for classifying point sets experiencing certain type...

This paper presents a new end-to-end signal classification method using the signed cumulative distribution transform (SCDT). We adopt a transport generative model to define the classification problem. We then make use of mathematical properties of the SCDT to render the problem easier in transform domain, and solve for the class of an unknown sampl...

The ability to perform quantitative and automated neurological assessment could enhance diagnosis and treatment in the pre-hospital setting, such as during telemedicine or emergency medical services (EMS) encounters. Such a tool could be developed by adapting clinically significant information such as symmetry of eye movement or conjugate eye movem...

There exist growing interests in intelligent systems for numerous medical imaging, image processing, and computer vision applications, such as face recognition, medical diagnosis, character recognition, and self-driving cars, among others. These applications usually require solving complex classification problems involving complex images with unkno...

Unlabelled: Automated eye-tracking technology could enhance diagnosis for many neurological diseases, including stroke. Current literature focuses on gaze estimation through a form of calibration. However, patients with neuro-ocular abnormalities may have difficulty completing a calibration procedure due to inattention or other neurological defici...

Deep convolutional neural networks (CNNs) are broadly considered to be state-of-the-art generic end-to-end image classification systems. However, they are known to underperform when training data are limited and thus require data augmentation strategies that render the method computationally expensive and not always effective. Rather than using a d...

Background Hematoma and perihematomal edema volumes are important radiographic markers in spontaneous intracerebral hemorrhage. Accurate, reliable, and efficient quantification of these volumes will be paramount to their utility as measures of treatment effect in future clinical studies. Both manual and semi-automated quantification methods of hema...

Background Current EMS stroke screening tools facilitate early detection and triage, but the tools' accuracy and reliability are limited and highly variable. An automated stroke screening tool could improve stroke outcomes by facilitating more accurate prehospital diagnosis and delivery. We hypothesize that a machine learning algorithm using video...

This paper presents a new end-to-end signal classification method using the signed cumulative distribution transform (SCDT). We adopt a transport-based generative model to define the classification problem. We then make use of mathematical properties of the SCDT to render the problem easier in transform domain, and solve for the class of an unknown...

We present a new method for face recognition from digital images acquired under varying illumination conditions. The method is based on mathematical modeling of local gradient distributions using the Radon Cumulative Distribution Transform (R-CDT). We demonstrate that lighting variations cause certain types of deformations of local image gradient d...

Deep convolutional neural networks (CNNs) are broadly considered to be state-of-the-art generic end-to-end image classification systems. However, they are known to underperform when training data are limited and thus require data augmentation strategies that render the method computationally expensive and not always effective. Rather than using a d...

A characteristic clinical feature of COVID-19 is the frequent incidence of microvascular thrombosis. In fact, COVID-19 autopsy reports have shown widespread thrombotic microangiopathy characterized by extensive diffuse microthrombi within peripheral capillaries and arterioles in lungs, hearts, and other organs, resulting in multiorgan failure. Howe...

This paper presents a new method to classify 1D signals using the signed cumulative distribution transform (SCDT). The proposed method exploits certain linearization properties of the SCDT to render the problem easier to solve in the SCDT space. The method uses the nearest subspace search technique in the SCDT domain to provide a non-iterative, eff...

A characteristic clinical feature of COVID-19 is the frequent occurrence of thrombotic events. Furthermore, many cases of multiorgan failure are thrombotic in nature. Since the outbreak of COVID-19, D-dimer testing has been used extensively to evaluate COVID-19-associated thrombosis, but does not provide a complete view of the disease because it pr...

A characteristic clinical feature of COVID-19 is the frequent occurrence of thrombotic events. Furthermore, many cases of multiorgan failure are thrombotic in nature. Since the outbreak of COVID-19, D-dimer testing has been used extensively to evaluate COVID-19-associated thrombosis, but does not provide a complete view of the disease because it pr...

Background: Posterior circulation stroke (PCS) accounts for ~20% of ischemic strokes. Existing EMS screening tools lack accuracy in the diagnosis of PCS. We aim to develop an automated screening tool to detect abnormal eye movements in patients presenting with PCS. Methods: As an initial step, we built a portable platform called RoADIE (Rolling App...

Automated eye tracking technology could enhance diagnosis and treatment for many neurological diseases, including posterior circulation stroke. Much of the current literature focuses on gaze estimation through a form of calibration. Unlike other fields, medicine has a clear need to better track eye symmetry during movement for better detection of a...

Objective: Facial weakness is a common sign of neurological diseases such as Bell's palsy and stroke. However, recognizing facial weakness still remains as a challenge, because it requires experience and neurological training. Methods: We propose a framework for facial weakness detection, which models the temporal dynamics of both shape and appe...

Facial weakness is a symptom commonly associated to lack of facial muscle control due to neurological injury. Several diseases are associated with facial weakness such as stroke and Bell's palsy. The use of digital imaging through mobile phones, tablets, personal computers and other devices could provide timely opportunity for detection, which if a...

Background: Prehospital stroke screening tools are essential to rapid diagnosis and treatment, but variable recognition of common stroke deficits by EMS providers limits the accuracy and reliability of these tools. An automated screening tool could yield more consistent assessments and reduce operator variability. We hypothesized that a video-based...

Background: Early recognition and treatment of stroke improves outcomes. Pre-hospital screening tools offer promise for early detection of acute stroke but have inconsistent performance. An automated screening tool could minimize inter-operator variability and operator error. We hypothesize that machine learning algorithms can assist in the detecti...

Continuous authentication is of great importance to maintain the security level of a system throughout the login session. The goal of this work is to investigate a trustworthy, continuous, and non-contact user authentication approach based on a heart-related biometric that works in a daily-life environment. To this end, we present a novel, continuo...

Quality of sleep is an important indicator of health and well being. Recent developments in the field of in-home sleep monitoring have the potential to enhance a person's sleeping experience and contribute to an overall sense of well being. Existing in-home sleep monitoring devices either fail to provide adequate sleep information or are obtrusive...

Gait analysis is an important medical diagnostic process and has many applications in healthcare, rehabilitation, therapy, and exercise training. However, typical gait analysis has to be performed in a gait laboratory, which is inaccessible for a large population and cannot provide natural gait measures. In this paper, we present a novel sensor dev...

The gender recognition is essential and critical for many applications in the commercial domains such as applications of human-computer interaction and computer-aided physiological or psychological analysis, since it contains a wide range of information regarding the characteristics difference between male and female. Some have proposed various app...

The gender recognition is essential and critical for many applications in the commercial domains such as applications of human-computer interaction and computer-aided physiological or psychological analysis, since it contains a wide range of information regarding the characteristics difference between male and female. Some have proposed various app...

Gender contains a wide range of information regarding to the characteristics difference between male and female. Successful gender recognition is essential and critical for many applications in the commercial domains such as applications of human-computer interaction and computer-aided physiological or psychological analysis. Some have proposed var...

Health effects attributed to air pollution, especially ambient fine particulate matter (PM2.5), become a global issue. The central environment monitoring networks provide limited spatial coverage and no contextual information. However , there is no solution to take contextual information, such as environmental and user behavioral factors, into acco...

Sleep monitoring is receiving increased attention in the healthcare community, because the quality of sleep has a great impact on human health. Existing in-home sleep monitoring devices are either obtrusive to the user or cannot provide adequate sleep information. To this end, we present SleepSense, a contactless and low-cost sleep monitoring syste...