My research lies at the intersection of machine learning, computer vision, medical image analysis, and computational neuroscience. I work on the automatic analysis of human activities and behaviors from videos and further connect how humans perform actions to the brain by also looking at magnetic resonance images. I explore explainable machine learning algorithms for understanding the underlying factors of neurodegenerative and neuropsychiatric diseases on the brain as well as their ramifications for everyday life.
Instructor, Psychiatry and Behavioral Sciences
Postdoctoral Research Associate, University of North Carolina at Chapel Hill, Machine Learning and Medical Imaging (2017)
Research Scholar, Carnegie Mellon University, Computer Vision (2012)
Current Research and Scholarly Interests
In a board outline, my research lies in the intersection of machine learning, computer vision, neuroimaging and computational neuroscience. Particularly, my research focuses on the investigation of different computational and statistical learning-based methods in processing both natural and biomedical images to extract semantics from the underlying visual content. Machine learning, statistics, signal and image processing, neuroscience, computer vision and neuroimaging have conventionally evolved independently to tackle problems from different perspectives. Occasionally, these concepts neglected each other, while they can offer complementary viewpoints. In the recent years, these fields have begun to intertwine, and it is increasingly becoming clear that we need to make use of multidisciplinary research to better process large-scale visual data. I consider my research interests and direction as located at the intersection of all the aforementioned fields.
Starting my position at the Biomedical Research Imaging Center (BRIC) in the University of North Carolina-Chapel Hill, my main research was focused on expanding my skillset and using my knowledge in machine learning and visual data analysis on the diagnosis of neurodegenerative diseases, and prediction of brain development throughout early years of life, based on neuroimaging data. Although neurodegenerative diseases manifest with diverse pathological features, the cellular level processes resemble similar structures. Therefore, data-driven machine learning methods can lead to great achievements and solve these problems accordingly. I have contributed to the critical studies on these diseases, including Parkinson's Progression Markers Initiative (PPMI) and Alzheimer's Disease Neuroimaging Initiative (ADNI). One of the goals of neuroscience and cognitive sciences is to understand how the brain works. Due to many different factors like technological limitations, this goal remains elusive. Over the past decade, remarkable advances in both hardware and software aspects have established new possibilities for understanding the brain.
Continuing my research at Stanford University, I believe my research advances computational science in identifying biomedical phenotypes that accelerate detection, understanding, and treatment of medical diseases and specifically neuropsychiatric disorders. Recently, I have started to use my knowledge and expertise in the multidisciplinary fields of machine learning and computational neuroscience to analyses brain images for gaining more insight to the human immunodeficiency virus (HIV) infection and alcoholism, along with their comorbidity. Each of these disorders carries liability for disruption of brain structure integrity. Furthermore, both HIV infection and alcoholism reduce health-related quality of life, and their co-occurrence is highly prevalent. However, few studies examined the potentially heightened burden of disease comorbidity, which often leads to cognitive impairments. I sought to create machine learning techniques to improve the mechanistic understating of their comorbidity effects in the brain.
- Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction IEEE ROBOTICS AND AUTOMATION LETTERS 2020; 5 (2): 3485–92
- Mammographic mass segmentation using multichannel and multiscale fully convolutional networks INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2020
FCN Based Label Correction for Multi-Atlas Guided Organ Segmentation.
Segmentation of medical images using multiple atlases has recently gained immense attention due to their augmented robustness against variabilities across different subjects. These atlas-based methods typically comprise of three steps: atlas selection, image registration, and finally label fusion. Image registration is one of the core steps in this process, accuracy of which directly affects the final labeling performance. However, due to inter-subject anatomical variations, registration errors are inevitable. The aim of this paper is to develop a deep learning-based confidence estimation method to alleviate the potential effects of registration errors. We first propose a fully convolutional network (FCN) with residual connections to learn the relationship between the image patch pair (i.e., patches from the target subject and the atlas) and the related label confidence patch. With the obtained label confidence patch, we can identify the potential errors in the warped atlas labels and correct them. Then, we use two label fusion methods to fuse the corrected atlas labels. The proposed methods are validated on a publicly available dataset for hippocampus segmentation. Experimental results demonstrate that our proposed methods outperform the state-of-the-art segmentation methods.
View details for DOI 10.1007/s12021-019-09448-5
View details for PubMedID 31898145
- Population-guided large margin classifier for high-dimension low-sample-size problems PATTERN RECOGNITION 2020; 97
High-Resolution Encoder-Decoder Networks for Low-Contrast Medical Image Segmentation.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Automatic image segmentation is an essential step for many medical image analysis applications, include computer-aided radiation therapy, disease diagnosis, and treatment effect evaluation. One of the major challenges for this task is the blurry nature of medical images (e.g., CT, MR and, microscopic images), which can often result in low-contrast and vanishing boundaries. With the recent advances in convolutional neural networks, vast improvements have been made for image segmentation, mainly based on the skip-connection-linked encoder-decoder deep architectures. However, in many applications (with adjacent targets in blurry images), these models often fail to accurately locate complex boundaries and properly segment tiny isolated parts. In this paper, we aim to provide a method for blurry medical image segmentation and argue that skip connections are not enough to help accurately locate indistinct boundaries. Accordingly, we propose a novel high-resolution multi-scale encoder-decoder network (HMEDN), in which multi-scale dense connections are introduced for the encoder-decoder structure to finely exploit comprehensive semantic information. Besides skip connections, extra deeply-supervised high-resolution pathways (comprised of densely connected dilated convolutions) are integrated to collect high-resolution semantic information for accurate boundary localization. These pathways are paired with a difficulty-guided cross-entropy loss function and a contour regression task to enhance the quality of boundary detection. Extensive experiments on a pelvic CT image dataset, a multi-modal brain tumor dataset, and a cell segmentation dataset show the effectiveness of our method for 2D/3D semantic segmentation and 2D instance segmentation, respectively. Our experimental results also show that besides increasing the network complexity, raising the resolution of semantic feature maps can largely affect the overall model performance. For different tasks, finding a balance between these two factors can further improve the performance of the corresponding network.
View details for DOI 10.1109/TIP.2019.2919937
View details for PubMedID 31226074
Infant Brain Development Prediction With Latent Partial Multi-View Representation Learning
IEEE TRANSACTIONS ON MEDICAL IMAGING
2019; 38 (4): 909–18
The early postnatal period witnesses rapid and dynamic brain development. However, the relationship between brain anatomical structure and cognitive ability is still unknown. Currently, there is no explicit model to characterize this relationship in the literature. In this paper, we explore this relationship by investigating the mapping between morphological features of the cerebral cortex and cognitive scores. To this end, we introduce a multi-view multi-task learning approach to intuitively explore complementary information from different time-points and handle the missing data issue in longitudinal studies simultaneously. Accordingly, we establish a novel model, latent partial multi-view representation learning. Our approach regards data from different time-points as different views and constructs a latent representation to capture the complementary information from incomplete time-points. The latent representation explores the complementarity across different time-points and improves the accuracy of prediction. The minimization problem is solved by the alternating direction method of multipliers. Experimental results on both synthetic and real data validate the effectiveness of our proposed algorithm.
View details for DOI 10.1109/TMI.2018.2874964
View details for Web of Science ID 000463608000004
View details for PubMedID 30307859
View details for PubMedCentralID PMC6450718
Novel Machine Learning Identifies Brain Patterns Distinguishing Diagnostic Membership of Human Immunodeficiency Virus, Alcoholism, and Their Comorbidity of Individuals.
Biological psychiatry. Cognitive neuroscience and neuroimaging
The incidence of alcohol use disorder (AUD) in human immunodeficiency virus (HIV) infection is twice that of the rest of the population. This study documents complex radiologically identified, neuroanatomical effects of AUD+HIV comorbidity by identifying structural brain systems that predicted diagnosis on an individual basis. Applying novel machine learning analysis to 549 participants (199 control subjects, 222 with AUD, 68 with HIV, 60 with AUD+HIV), 298 magnetic resonance imaging brain measurements were automatically reduced to small subsets per group. Significance of each diagnostic pattern was inferred from its accuracy in predicting diagnosis and performance on six cognitive measures. While all three diagnostic patterns predicted the learning and memory score, the AUD+HIV pattern was the largest and had the highest predication accuracy (78.1%). Providing a roadmap for analyzing large, multimodal datasets, the machine learning analysis revealed imaging phenotypes that predicted diagnostic membership of magnetic resonance imaging scans of individuals with AUD, HIV, and their comorbidity.
View details for DOI 10.1016/j.bpsc.2019.02.003
View details for PubMedID 30982583
Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
2019; 41 (2): 515–22
Discriminative methods commonly produce models with relatively good generalization abilities. However, this advantage is challenged in real-world applications (e.g., medical image analysis problems), in which there often exist outlier data points (sample-outliers) and noises in the predictor values (feature-noises). Methods robust to both types of these deviations are somewhat overlooked in the literature. We further argue that denoising can be more effective, if we learn the model using all the available labeled and unlabeled samples, as the intrinsic geometry of the sample manifold can be better constructed using more data points. In this paper, we propose a semi-supervised robust discriminative classification method based on the least-squares formulation of linear discriminant analysis to detect sample-outliers and feature-noises simultaneously, using both labeled training and unlabeled testing data. We conduct several experiments on a synthetic, some benchmark semi-supervised learning, and two brain neurodegenerative disease diagnosis datasets (for Parkinson's and Alzheimer's diseases). Specifically for the application of neurodegenerative diseases diagnosis, incorporating robust machine learning methods can be of great benefit, due to the noisy nature of neuroimaging data. Our results show that our method outperforms the baseline and several state-of-the-art methods, in terms of both accuracy and the area under the ROC curve.
View details for DOI 10.1109/TPAMI.2018.2794470
View details for Web of Science ID 000456150600018
View details for PubMedID 29994560
View details for PubMedCentralID PMC6050136
Multi-task prediction of infant cognitive scores from longitudinal incomplete neuroimaging data
2019; 185: 783–92
Early postnatal brain undergoes a stunning period of development. Over the past few years, research on dynamic infant brain development has received increased attention, exhibiting how important the early stages of a child's life are in terms of brain development. To precisely chart the early brain developmental trajectories, longitudinal studies with data acquired over a long-enough period of infants' early life is essential. However, in practice, missing data from different time point(s) during the data gathering procedure is often inevitable. This leads to incomplete set of longitudinal data, which poses a major challenge for such studies. In this paper, prediction of multiple future cognitive scores with incomplete longitudinal imaging data is modeled into a multi-task machine learning framework. To efficiently learn this model, we account for selection of informative features (i.e., neuroimaging morphometric measurements for different time points), while preserving the structural information and the interrelation between these multiple cognitive scores. Several experiments are conducted on a carefully acquired in-house dataset, and the results affirm that we can predict the cognitive scores measured at the age of four years old, using the imaging data of earlier time points, as early as 24 months of age, with a reasonable performance (i.e., root mean square error of 0.18).
View details for DOI 10.1016/j.neuroimage.2018.04.052
View details for Web of Science ID 000451628200066
View details for PubMedID 29709627
View details for PubMedCentralID PMC6204112
Difficulty-Aware Attention Network with Confidence Learning for Medical Image Segmentation
ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2019: 1085–92
View details for Web of Science ID 000485292601012
UNSUPERVISED FEATURE RANKING AND SELECTION BASED ON AUTOENCODERS
IEEE. 2019: 3172–76
View details for Web of Science ID 000482554003079
- Variational Autoencoder with Truncated Mixture of Gaussians for Functional Connectivity Analysis SPRINGER INTERNATIONAL PUBLISHING AG. 2019: 867–79
- AVID: Adversarial Visual Irregularity Detection SPRINGER INTERNATIONAL PUBLISHING AG. 2019: 488–505
- Action-Agnostic Human Pose Forecasting IEEE. 2019: 1423–32
- Chained regularization for identifying brain patterns specific to HIV infection NEUROIMAGE 2018; 183: 425–37
Chained regularization for identifying brain patterns specific to HIV infection.
Human Immunodeficiency Virus (HIV) infection continues to have major adverse public health and clinical consequences despite the effectiveness of combination Antiretroviral Therapy (cART) in reducing HIV viral load and improving immune function. As successfully treated individuals with HIV infection age, their cognition declines faster than reported for normal aging. This phenomenon underlines the importance of improving long-term care, which requires better understanding of the impact of HIV on the brain. In this paper, automated identification of patients and brain regions affected by HIV infection are modeled as a classification problem, whose solution is determined in two steps within our proposed Chained-Regularization framework. The first step focuses on selecting the HIV pattern (i.e., the most informative constellation of brain region measurements for distinguishing HIV infected subjects from healthy controls) by constraining the search for the optimal parameter setting of the classifier via group sparsity (ℓ2,1-norm). The second step improves classification accuracy by constraining the parameterization with respect to the selected measurements and the Euclidean regularization (ℓ2-norm). When applied to the cortical and subcortical structural Magnetic Resonance Images (MRI) measurements of 65 controls and 65 HIV infected individuals, this approach is more accurate in distinguishing the two cohorts than more common models. Finally, the brain regions of the identified HIV pattern concur with the HIV literature that uses traditional group analysis models.
View details for PubMedID 30138676
Exploring diagnosis and imaging biomarkers of Parkinson's disease via iterative canonical correlation analysis based feature selection
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
2018; 67: 21–29
Parkinson's disease (PD) is a neurodegenerative disorder that progressively hampers the brain functions and leads to various movement and non-motor symptoms. However, it is difficult to attain early-stage PD diagnosis based on the subjective judgment of physicians in clinical routines. Therefore, automatic and accurate diagnosis of PD is highly demanded, so that the corresponding treatment can be implemented more appropriately. In this paper, we focus on finding the most discriminative features from different brain regions in PD through T1-weighted MR images, which can help the subsequent PD diagnosis. Specifically, we proposed a novel iterative canonical correlation analysis (ICCA) feature selection method, aiming at exploiting MR images in a more comprehensive manner and fusing features of different types into a common space. To state succinctly, we first extract the feature vectors from the gray matter and the white matter tissues separately, represented as insights of two different anatomical feature spaces for the subject's brain. The ICCA feature selection method aims at iteratively finding the optimal feature subset from two sets of features that have inherent high correlation with each other. In experiments we have conducted thorough investigations on the optimal feature set extracted by our ICCA method. We also demonstrate that using the proposed feature selection method, the PD diagnosis performance is further improved, and also outperforms many state-of-the-art methods.
View details for DOI 10.1016/j.compmedimag.2018.04.002
View details for Web of Science ID 000447358800003
View details for PubMedID 29702348
- End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 337–45
- Multi-label Transduction for Identifying Disease Comorbidity Patterns SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 575–83
INFANT BRAIN DEVELOPMENT PREDICTION WITH LATENT PARTIAL MULTI-VIEW REPRESENTATION LEARNING.
Proceedings. IEEE International Symposium on Biomedical Imaging
2018; 2018: 1048–51
The early postnatal period witnesses rapid and dynamic brain development. Understanding the cognitive development patterns can help identify various disorders at early ages of life and is essential for the health and well-being of children. This inspires us to investigate the relation between cognitive ability and the cerebral cortex by exploiting brain images in a longitudinal study. Specifically, we aim to predict the infant brain development status based on the morphological features of the cerebral cortex. For this goal, we introduce a multi-view multi-task learning approach to dexterously explore complementary information from different time points and handle the missing data simultaneously. Specifically, we establish a novel model termed as Latent Partial Multi-view Representation Learning. The approach regards data of different time points as different views, and constructs a latent representation to capture the complementary underlying information from different and even incomplete time points. It uncovers the latent representation that can be jointly used to learn the prediction model. This formulation elegantly explores the complementarity, effectively reduces the redundancy of different views, and improves the accuracy of prediction. The minimization problem is solved by the Alternating Direction Method of Multipliers (ADMM). Experimental results on real data validate the proposed method.
View details for PubMedID 30464798
View details for PubMedCentralID PMC6242279
Landmark-based deep multi-instance learning for brain disease diagnosis
MEDICAL IMAGE ANALYSIS
2018; 43: 157–68
In conventional Magnetic Resonance (MR) image based methods, two stages are often involved to capture brain structural information for disease diagnosis, i.e., 1) manually partitioning each MR image into a number of regions-of-interest (ROIs), and 2) extracting pre-defined features from each ROI for diagnosis with a certain classifier. However, these pre-defined features often limit the performance of the diagnosis, due to challenges in 1) defining the ROIs and 2) extracting effective disease-related features. In this paper, we propose a landmark-based deep multi-instance learning (LDMIL) framework for brain disease diagnosis. Specifically, we first adopt a data-driven learning approach to discover disease-related anatomical landmarks in the brain MR images, along with their nearby image patches. Then, our LDMIL framework learns an end-to-end MR image classifier for capturing both the local structural information conveyed by image patches located by landmarks and the global structural information derived from all detected landmarks. We have evaluated our proposed framework on 1526 subjects from three public datasets (i.e., ADNI-1, ADNI-2, and MIRIAD), and the experimental results show that our framework can achieve superior performance over state-of-the-art approaches.
View details for DOI 10.1016/j.media.2017.10.005
View details for Web of Science ID 000418627400012
View details for PubMedID 29107865
View details for PubMedCentralID PMC6203325
- Adversarially Learned One-Class Classifier for Novelty Detection IEEE. 2018: 3379–88
Multi-Layer Multi-View Classification for Alzheimer's Disease Diagnosis
ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2018: 4406–13
In this paper, we propose a novel multi-view learning method for Alzheimer's Disease (AD) diagnosis, using neuroimaging and genetics data. Generally, there are several major challenges associated with traditional classification methods on multi-source imaging and genetics data. First, the correlation between the extracted imaging features and class labels is generally complex, which often makes the traditional linear models ineffective. Second, medical data may be collected from different sources (i.e., multiple modalities of neuroimaging data, clinical scores or genetics measurements), therefore, how to effectively exploit the complementarity among multiple views is of great importance. In this paper, we propose a Multi-Layer Multi-View Classification (ML-MVC) approach, which regards the multi-view input as the first layer, and constructs a latent representation to explore the complex correlation between the features and class labels. This captures the high-order complementarity among different views, as we exploit the underlying information with a low-rank tensor regularization. Intrinsically, our formulation elegantly explores the nonlinear correlation together with complementarity among different views, and thus improves the accuracy of classification. Finally, the minimization problem is solved by the Alternating Direction Method of Multipliers (ADMM). Experimental results on Alzheimers Disease Neuroimaging Initiative (ADNI) data sets validate the effectiveness of our proposed method.
View details for Web of Science ID 000485488904061
View details for PubMedID 30416868
View details for PubMedCentralID PMC6223635
- Fine-Grained Segmentation Using Hierarchical Dilated Neural Networks SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 488–96
- Predictive Modeling of Longitudinal Data for Alzheimer's Disease Diagnosis Using RNNs SPRINGER INTERNATIONAL PUBLISHING AG. 2018: 112–19
Structured Prediction with Short/Long-Range Dependencies for Human Activity Recognition from Depth Skeleton Data
IEEE. 2017: 560–67
View details for Web of Science ID 000426978200079