222 citations · 637 across the 23 of their papers we have counts for
23 papers
Learning correspondences of cardiac motion from images using biomechanics-informed modeling
Xiaoran Zhang, Chenyu You, Shawn Ahn +3
Learning spatial-temporal correspondences in cardiac motion from images is important for understanding the underlying dynamics of cardiac anatomical structures. Many methods explic…
Surrogate Gap Minimization Improves Sharpness-Aware Training
Juntang Zhuang, Boqing Gong, Liangzhe Yuan +6
The recently proposed Sharpness-Aware Minimization (SAM) improves generalization by minimizing a \textit{perturbed loss} defined as the maximum loss within a neighborhood in the pa…
DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum Optimization
Boyue Li, Zhize Li, Yuejie Chi
Emerging applications in multi-agent environments such as internet-of-things, networked sensing, autonomous systems and federated learning, call for decentralized algorithms for fi…
Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity
Nicha C. Dvornek, Xiaoxiao Li, Juntang Zhuang +2
Heterogeneous presentation of a neurological disorder suggests potential differences in the underlying pathophysiological changes that occur in the brain. We propose to model heter…
MALI: A memory efficient and reverse accurate integrator for Neural ODEs
Juntang Zhuang, Nicha C. Dvornek, Sekhar Tatikonda +1
Neural ordinary differential equations (Neural ODEs) are a new family of deep-learning models with continuous depth. However, the numerical estimation of the gradient in the contin…
Multiple-shooting adjoint method for whole-brain dynamic causal modeling
Juntang Zhuang, Nicha Dvornek, Sekhar Tatikonda +3
Dynamic causal modeling (DCM) is a Bayesian framework to infer directed connections between compartments, and has been used to describe the interactions between underlying neural p…
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
Juntang Zhuang, Tommy Tang, Yifan Ding +4
Most popular optimizers for deep learning can be broadly categorized as adaptive methods (e.g. Adam) and accelerated schemes (e.g. stochastic gradient descent (SGD) with momentum).…
Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis
Xiaoxiao Li, Yuan Zhou, Nicha C. Dvornek +4
Understanding how certain brain regions relate to a specific neurological disorder has been an important area of neuroimaging research. A promising approach to identify the salient…
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE
Juntang Zhuang, Nicha Dvornek, Xiaoxiao Li +3
Neural ordinary differential equations (NODEs) have recently attracted increasing attention; however, their empirical performance on benchmark tasks (e.g. image classification) are…
Neuropsychiatric Disease Classification Using Functional Connectomics -- Results of the Connectomics in NeuroImaging Transfer Learning Challenge
Markus D. Schirmer, Archana Venkataraman, Islem Rekik +23
Large, open-source consortium datasets have spurred the development of new and increasingly powerful machine learning approaches in brain connectomics. However, one key question re…
2018 Robotic Scene Segmentation Challenge
Max Allan, Satoshi Kondo, Sebastian Bodenstedt +38
In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward ki…
Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI
Nicha C. Dvornek, Xiaoxiao Li, Juntang Zhuang +1
Recurrent neural networks (RNNs) were designed for dealing with time-series data and have recently been used for creating predictive models from functional magnetic resonance imagi…
Decision Explanation and Feature Importance for Invertible Networks
Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li +2
Deep neural networks are vulnerable to adversarial attacks and hard to interpret because of their black-box nature. The recently proposed invertible network is able to accurately r…
Domain-Agnostic Learning with Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation
Junlin Yang, Nicha C. Dvornek, Fan Zhang +4
Domain Adaptation (DA) has the potential to greatly help the generalization of deep learning models. However, the current literature usually assumes to transfer the knowledge from…
Graph Embedding Using Infomax for ASD Classification and Brain Functional Difference Detection
Xiaoxiao Li, Nicha C. Dvornek, Juntang Zhuang +2
Significant progress has been made using fMRI to characterize the brain changes that occur in ASD, a complex neuro-developmental disorder. However, due to the high dimensionality a…
Invertible Network for Classification and Biomarker Selection for ASD
Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li +2
Determining biomarkers for autism spectrum disorder (ASD) is crucial to understanding its mechanisms. Recently deep learning methods have achieved success in the classification tas…
Graph Neural Network for Interpreting Task-fMRI Biomarkers
Xiaoxiao Li, Nicha C. Dvornek, Yuan Zhou +3
Finding the biomarkers associated with ASD is helpful for understanding the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatment. A promis…
Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery
Xiaoxiao Li, Nicha C. Dvornek, Yuan Zhou +3
Discovering imaging biomarkers for autism spectrum disorder (ASD) is critical to help explain ASD and predict or monitor treatment outcomes. Toward this end, deep learning classifi…
ShelfNet for Fast Semantic Segmentation
Juntang Zhuang, Junlin Yang, Lin Gu +1
In this paper, we present ShelfNet, a novel architecture for accurate fast semantic segmentation. Different from the single encoder-decoder structure, ShelfNet has multiple encoder…
Prediction of severity and treatment outcome for ASD from fMRI
Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li +2
Autism spectrum disorder (ASD) is a complex neurodevelopmental syndrome. Early diagnosis and precise treatment are essential for ASD patients. Although researchers have built many…
LadderNet: Multi-path networks based on U-Net for medical image segmentation
Juntang Zhuang
U-Net has been providing state-of-the-art performance in many medical image segmentation problems. Many modifications have been proposed for U-Net, such as attention U-Net, recurre…
Prediction of treatment outcome for autism from structure of the brain based on sure independence screening
Juntang Zhuang, Nicha C. Dvornek, Qingyu Zhao +3
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and behavioral treatment interventions have shown promise for young children with ASD. However, there is li…
Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI
Xiaoxiao Li, Nicha C. Dvornek, Juntang Zhuang +2
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder. Finding the biomarkers associated with ASD is extremely helpful to understand the underlying roots of the d…