Search Results for author: James S. Duncan

Found 57 papers, 16 papers with code

Assessment of Clonal Hematopoiesis of Indeterminate Potential from Cardiac Magnetic Resonance Imaging using Deep Learning in a Cardio-oncology Population

no code implementations26 Jun 2024 Sangeon Ryu, Shawn Ahn, Jeacy Espinoza, Alokkumar Jha, Stephanie Halene, James S. Duncan, Jennifer M Kwan, Nicha C. Dvornek

Background: We propose a novel method to identify who may likely have clonal hematopoiesis of indeterminate potential (CHIP), a condition characterized by the presence of somatic mutations in hematopoietic stem cells without detectable hematologic malignancy, using deep learning techniques.

Spectral Brain Graph Neural Network for Prediction of Anxiety in Children with Autism Spectrum Disorder

no code implementations23 Apr 2024 Peiyu Duan, Nicha C. Dvornek, Jiyao Wang, Jeffrey Eilbott, Yuexi Du, Denis G. Sukhodolsky, James S. Duncan

We proposed SpectBGNN, a graph-based network, which uses spectral features and integrates graph spectral filtering layers to extract hidden information.

Graph Neural Network

Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation

no code implementations6 Apr 2024 Yinchi Zhou, Tianqi Chen, Jun Hou, Huidong Xie, Nicha C. Dvornek, S. Kevin Zhou, David L. Wilson, James S. Duncan, Chi Liu, Bo Zhou

To reduce the required number of iterations and ensure robust performance, our method first obtains a conditional GAN-generated prior image that will be used for the efficient reverse translation with a DM in the subsequent step.

Image-to-Image Translation Translation

Calibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotations

1 code implementation CVPR 2024 Chenyu You, Yifei Min, Weicheng Dai, Jasjeet S. Sekhon, Lawrence Staib, James S. Duncan

As a piloting study, this work focuses on exploring mitigating the reliance on spurious features for CLIP without using any group annotation.

Contrastive Learning

Dual-Domain Coarse-to-Fine Progressive Estimation Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT

1 code implementation23 Jan 2024 Xiongchao Chen, Bo Zhou, Xueqi Guo, Huidong Xie, Qiong Liu, James S. Duncan, Albert J. Sinusas, Chi Liu

Additionally, Computed Tomography (CT) is commonly used to derive attenuation maps ($\mu$-maps) for attenuation correction (AC) of cardiac SPECT, but it will introduce additional radiation exposure and SPECT-CT misalignments.

Computed Tomography (CT) Denoising +1

Heteroscedastic Uncertainty Estimation Framework for Unsupervised Registration

no code implementations1 Dec 2023 Xiaoran Zhang, Daniel H. Pak, Shawn S. Ahn, Xiaoxiao Li, Chenyu You, Lawrence H. Staib, Albert J. Sinusas, Alex Wong, James S. Duncan

To mitigate this, we propose a framework for heteroscedastic image uncertainty estimation that can adaptively reduce the influence of regions with high uncertainty during unsupervised registration.

Image Registration Medical Image Registration

Adaptive Correspondence Scoring for Unsupervised Medical Image Registration

no code implementations1 Dec 2023 Xiaoran Zhang, John C. Stendahl, Lawrence Staib, Albert J. Sinusas, Alex Wong, James S. Duncan

As the unsupervised learning scheme relies on intensity constancy between images to establish correspondence for reconstruction, this introduces spurious error residuals that are not modeled by the typical training objective.

Image Reconstruction Medical Image Registration +1

Preserved Edge Convolutional Neural Network for Sensitivity Enhancement of Deuterium Metabolic Imaging (DMI)

no code implementations8 Sep 2023 Siyuan Dong, Henk M. De Feyter, Monique A. Thomas, Robin A. de Graaf, James S. Duncan

The proposed processing method, PReserved Edge ConvolutIonal neural network for Sensitivity Enhanced DMI (PRECISE-DMI), was applied to simulation studies and in vivo experiments to evaluate the anticipated improvements in SNR and investigate the potential for inaccuracies.

Learning Sequential Information in Task-based fMRI for Synthetic Data Augmentation

no code implementations29 Aug 2023 Jiyao Wang, Nicha C. Dvornek, Lawrence H. Staib, James S. Duncan

Insufficiency of training data is a persistent issue in medical image analysis, especially for task-based functional magnetic resonance images (fMRI) with spatio-temporal imaging data acquired using specific cognitive tasks.

Data Augmentation

Copy Number Variation Informs fMRI-based Prediction of Autism Spectrum Disorder

no code implementations8 Aug 2023 Nicha C. Dvornek, Catherine Sullivan, James S. Duncan, Abha R. Gupta

We demonstrate that our attention-based model combining genetic information, demographic data, and functional magnetic resonance imaging results in superior prediction performance compared to other multimodal approaches.

severity prediction

Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts

1 code implementation6 Apr 2023 Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, James S. Duncan

Integrating high-level semantically correlated contents and low-level anatomical features is of central importance in medical image segmentation.

Image Segmentation Medical Image Segmentation +3

ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast

2 code implementations5 Apr 2023 Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, Jasjeet S. Sekhon, James S. Duncan

In this work, we present ACTION++, an improved contrastive learning framework with adaptive anatomical contrast for semi-supervised medical segmentation.

Contrastive Learning Image Segmentation +2

FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising

1 code implementation2 Apr 2023 Bo Zhou, Huidong Xie, Qiong Liu, Xiongchao Chen, Xueqi Guo, Zhicheng Feng, Jun Hou, S. Kevin Zhou, Biao Li, Axel Rominger, Kuangyu Shi, James S. Duncan, Chi Liu

While previous federated learning (FL) algorithms enable multi-institution collaborative training without the need of aggregating local data, addressing the large domain shift in the application of multi-institutional low-count PET denoising remains a challenge and is still highly under-explored.

Denoising Personalized Federated Learning

Meta-information-aware Dual-path Transformer for Differential Diagnosis of Multi-type Pancreatic Lesions in Multi-phase CT

no code implementations2 Mar 2023 Bo Zhou, Yingda Xia, Jiawen Yao, Le Lu, Jingren Zhou, Chi Liu, James S. Duncan, Ling Zhang

Accurate detection, segmentation, and differential diagnosis of the full taxonomy of pancreatic lesions, i. e., normal, seven major types of lesions, and other lesions, is critical to aid the clinical decision-making of patient management and treatment.

Classification Decision Making +2

Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction

no code implementations18 Feb 2023 Bo Zhou, Jo Schlemper, Neel Dey, Seyed Sadegh Mohseni Salehi, Kevin Sheth, Chi Liu, James S. Duncan, Michal Sofka

To this end, we present a fully self-supervised approach for accelerated non-Cartesian MRI reconstruction which leverages self-supervision in both k-space and image domains.

MRI Reconstruction Self-Supervised Learning

Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels

no code implementations27 Sep 2022 Chenyu You, Weicheng Dai, Fenglin Liu, Yifei Min, Haoran Su, Xiaoran Zhang, Xiaoxiao Li, David A. Clifton, Lawrence Staib, James S. Duncan

Blindly leveraging all pixels in training hence can lead to the data imbalance issues, and cause deteriorated performance; (2) consistency: it remains unclear whether a segmentation model has learned meaningful and yet consistent anatomical features due to the intra-class variations between different anatomical features; and (3) diversity: the intra-slice correlations within the entire dataset have received significantly less attention.

Anatomy Contrastive Learning +4

Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation

1 code implementation6 Jun 2022 Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, James S. Duncan

In this work, we present ACTION, an Anatomical-aware ConTrastive dIstillatiON framework, for semi-supervised medical image segmentation.

Contrastive Learning Image Segmentation +3

Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation

no code implementations3 Jun 2022 Chenyu You, Jinlin Xiang, Kun Su, Xiaoran Zhang, Siyuan Dong, John Onofrey, Lawrence Staib, James S. Duncan

Many medical datasets have recently been created for medical image segmentation tasks, and it is natural to question whether we can use them to sequentially train a single model that (1) performs better on all these datasets, and (2) generalizes well and transfers better to the unknown target site domain.

Image Segmentation Incremental Learning +4

Momentum Centering and Asynchronous Update for Adaptive Gradient Methods

2 code implementations NeurIPS 2021 Juntang Zhuang, Yifan Ding, Tommy Tang, Nicha Dvornek, Sekhar Tatikonda, James S. Duncan

We demonstrate that ACProp has a convergence rate of $O(\frac{1}{\sqrt{T}})$ for the stochastic non-convex case, which matches the oracle rate and outperforms the $O(\frac{logT}{\sqrt{T}})$ rate of RMSProp and Adam.

Image Classification

SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation

no code implementations13 Aug 2021 Chenyu You, Yuan Zhou, Ruihan Zhao, Lawrence Staib, James S. Duncan

However, most existing learning-based approaches usually suffer from limited manually annotated medical data, which poses a major practical problem for accurate and robust medical image segmentation.

Data Augmentation Image Generation +5

Anatomy-Constrained Contrastive Learning for Synthetic Segmentation without Ground-truth

1 code implementation12 Jul 2021 Bo Zhou, Chi Liu, James S. Duncan

The manual efforts can be alleviated if the manual segmentation in one imaging modality (e. g., CT) can be utilized to train a segmentation network in another imaging modality (e. g., CBCT/MRI/PET).

Anatomy Contrastive Learning +1

Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation

no code implementations14 May 2021 Chenyu You, Ruihan Zhao, Lawrence Staib, James S. Duncan

In this work, we present a novel Contrastive Voxel-wise Representation Learning (CVRL) method to effectively learn low-level and high-level features by capturing 3D spatial context and rich anatomical information along both the feature and the batch dimensions.

Contrastive Learning Image Segmentation +4

Estimating Reproducible Functional Networks Associated with Task Dynamics using Unsupervised LSTMs

no code implementations6 May 2021 Nicha C. Dvornek, Pamela Ventola, James S. Duncan

We demonstrate that the functional networks learned by the LSTM model are more strongly associated with the task activity and dynamics compared to other approaches.

Time Series Time Series Analysis

Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity

no code implementations15 Apr 2021 Nicha C. Dvornek, Xiaoxiao Li, Juntang Zhuang, Pamela Ventola, James S. Duncan

Heterogeneous presentation of a neurological disorder suggests potential differences in the underlying pathophysiological changes that occur in the brain.

Time Series Time Series Analysis

Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration

1 code implementation14 Apr 2021 Bo Zhou, Zachary Augenfeld, Julius Chapiro, S. Kevin Zhou, Chi Liu, James S. Duncan

Our experimental results on in-house TACE patient data demonstrated that our APA2Seg-Net can generate robust CBCT and MR liver segmentation, and the anatomy-guided registration framework with these segmenters can provide high-quality multimodal registrations.

Anatomy Domain Adaptation +3

AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients

8 code implementations NeurIPS 2020 Juntang Zhuang, Tommy Tang, Yifan Ding, Sekhar Tatikonda, Nicha Dvornek, Xenophon Papademetris, James S. Duncan

Viewing the exponential moving average (EMA) of the noisy gradient as the prediction of the gradient at the next time step, if the observed gradient greatly deviates from the prediction, we distrust the current observation and take a small step; if the observed gradient is close to the prediction, we trust it and take a large step.

Image Classification Language Modelling

Limited View Tomographic Reconstruction Using a Deep Recurrent Framework with Residual Dense Spatial-Channel Attention Network and Sinogram Consistency

no code implementations3 Sep 2020 Bo Zhou, S. Kevin Zhou, James S. Duncan, Chi Liu

To derive quality reconstruction, previous state-of-the-art methods use UNet-like neural architectures to directly predict the full view reconstruction from limited view data; but these methods leave the deep network architecture issue largely intact and cannot guarantee the consistency between the sinogram of the reconstructed image and the acquired sinogram, leading to a non-ideal reconstruction.

A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises

no code implementations2 Aug 2020 S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, James S. Duncan, Bram van Ginneken, Anant Madabhushi, Jerry L. Prince, Daniel Rueckert, Ronald M. Summers

In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues.

Uncertainty Quantification

Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis

no code implementations29 Jul 2020 Xiaoxiao Li, Yuan Zhou, Nicha C. Dvornek, Muhan Zhang, Juntang Zhuang, Pamela Ventola, James S. Duncan

We propose an interpretable GNN framework with a novel salient region selection mechanism to determine neurological brain biomarkers associated with disorders.

Graph Neural Network

Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results

1 code implementation16 Jan 2020 Xiaoxiao Li, Yufeng Gu, Nicha Dvornek, Lawrence Staib, Pamela Ventola, James S. Duncan

However, to effectively train a high-quality deep learning model, the aggregation of a significant amount of patient information is required.

Domain Adaptation Federated Learning +2

Hepatocellular Carcinoma Intra-arterial Treatment Response Prediction for Improved Therapeutic Decision-Making

no code implementations1 Dec 2019 Junlin Yang, Nicha C. Dvornek, Fan Zhang, Julius Chapiro, MingDe Lin, Aaron Abajian, James S. Duncan

This work proposes a pipeline to predict treatment response to intra-arterial therapy of patients with Hepatocellular Carcinoma (HCC) for improved therapeutic decision-making.

Decision Making Graph Neural Network

Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI

no code implementations15 Oct 2019 Nicha C. Dvornek, Xiaoxiao Li, Juntang Zhuang, James S. Duncan

The addition of the generative model constrains the network to learn functional communities represented by the LSTM nodes that are both consistent with the data generation as well as useful for the classification task.

Classification General Classification +2

Decision Explanation and Feature Importance for Invertible Networks

1 code implementation30 Sep 2019 Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li, Junlin Yang, James S. Duncan

We can determine the decision boundary of a linear classifier in the feature space; since the transform is invertible, we can invert the decision boundary from the feature space to the input space.

Feature Importance

Ordinary differential equations on graph networks

no code implementations25 Sep 2019 Juntang Zhuang, Nicha Dvornek, Xiaoxiao Li, James S. Duncan

Inspired by neural ordinary differential equation (NODE) for data in the Euclidean domain, we extend the idea of continuous-depth models to graph data, and propose graph ordinary differential equation (GODE).

Graph Classification Graph Neural Network +1

Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery

no code implementations14 Dec 2018 Xiaoxiao Li, Nicha C. Dvornek, Yuan Zhou, Juntang Zhuang, Pamela Ventola, James S. Duncan

Cooperative game theory is advantageous here because it directly considers the interaction between features and can be applied to any machine learning method, making it a novel, more accurate way of determining instance-wise biomarker importance from deep learning models.

Feature Importance

Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI

no code implementations23 Aug 2018 Xiaoxiao Li, Nicha C. Dvornek, Juntang Zhuang, Pamela Ventola, James S. Duncan

Therefore, in this work, we address the problem of interpreting reliable biomarkers associated with identifying ASD; specifically, we propose a 2-stage method that classifies ASD and control subjects using fMRI images and interprets the saliency features activated by the classifier.

Decision Making

Prediction of Autism Treatment Response from Baseline fMRI using Random Forests and Tree Bagging

no code implementations24 May 2018 Nicha C. Dvornek, Daniel Yang, Archana Venkataraman, Pamela Ventola, Lawrence H. Staib, Kevin A. Pelphrey, James S. Duncan

We propose predicting patient response to PRT from baseline task-based fMRI by the novel application of a random forest and tree bagging strategy.

Active Contours with Group Similarity

no code implementations CVPR 2013 Xiaowei Zhou, Xiaojie Huang, James S. Duncan, Weichuan Yu

In this paper, we propose to use the group similarity of object shapes in multiple images as a prior to aid segmentation, which can be interpreted as an unsupervised approach of shape prior modeling.

Image Segmentation Semantic Segmentation

Cannot find the paper you are looking for? You can Submit a new open access paper.