1 code implementation • 9 Jun 2025 • Daniel H. Pak, Shubh Thaker, Kyle Baylous, Xiaoran Zhang, Danny Bluestein, James S. Duncan
High-quality volumetric meshing from medical images is a key bottleneck for physics-based simulations in personalized medicine.
no code implementations • 14 May 2025 • Anne-Marie Rickmann, Stephanie L. Thorn, Shawn S. Ahn, Supum Lee, Selen Uman, Taras Lysyy, Rachel Burns, Nicole Guerrera, Francis G. Spinale, Jason A. Burdick, Albert J. Sinusas, James S. Duncan
Cardiac image segmentation is an important step in many cardiac image analysis and modeling tasks such as motion tracking or simulations of cardiac mechanics.
no code implementations • 31 Mar 2025 • Xiaoran Zhang, Eric Z. Chen, Lin Zhao, Xiao Chen, Yikang Liu, Boris Maihe, James S. Duncan, Terrence Chen, Shanhui Sun
We propose a novel approach that adapts hierarchical vision foundation models for real-time ultrasound image segmentation.
no code implementations • 21 Feb 2025 • Peiyu Duan, Nicha C. Dvornek, Jiyao Wang, Lawrence H. Staib, James S. Duncan
To solve this problem, we introduce a causality-inspired deep learning model that uses time-series information from fMRI and captures causality among ROIs useful for ASD classification.
no code implementations • 15 Feb 2025 • Jiyao Wang, Nicha C. Dvornek, Peiyu Duan, Lawrence H. Staib, James S. Duncan
To resolve this complication, we propose a supervised task-aware network TA-GAT that jointly learns a general-purpose encoder and task-specific contextual information.
no code implementations • 1 Jan 2025 • Chenyu You, Haocheng Dai, Yifei Min, Jasjeet S. Sekhon, Sarang Joshi, James S. Duncan
This paper systematically shows the ubiquitous existence of spurious features in a small set of neurons within the network, providing the first-ever evidence that memorization may contribute to imbalanced group performance.
3 code implementations • 2 Dec 2024 • Nicholas Konz, Richard Osuala, Preeti Verma, YuWen Chen, Hanxue Gu, Haoyu Dong, Yaqian Chen, Andrew Marshall, Lidia Garrucho, Kaisar Kushibar, Daniel M. Lang, Gene S. Kim, Lars J. Grimm, John M. Lewin, James S. Duncan, Julia A. Schnabel, Oliver Diaz, Karim Lekadir, Maciej A. Mazurowski
Currently, metrics used for this task either rely on the (potentially biased) choice of some downstream task, such as segmentation, or adopt task-independent perceptual metrics (e. g., Fr\'echet Inception Distance/FID) from natural imaging, which we show insufficiently capture anatomical features.
no code implementations • 25 Oct 2024 • Siyuan Dong, Zhuotong Cai, Gilbert Hangel, Wolfgang Bogner, Georg Widhalm, Yaqing Huang, Qinghao Liang, Chenyu You, Chathura Kumaragamage, Robert K. Fulbright, Amit Mahajan, Amin Karbasi, John A. Onofrey, Robin A. de Graaf, James S. Duncan
Recently, diffusion models have demonstrated superior learning capability than other generative models in various tasks, but sampling from diffusion models requires iterating through a large number of diffusion steps, which is time-consuming.
no code implementations • 29 Jun 2024 • Caglar Ozturk, Daniel H. Pak, Luca Rosalia, Debkalpa Goswami, Mary E. Robakowski, Raymond McKay, Christopher T. Nguyen, James S. Duncan, Ellen T. Roche
Aortic stenosis (AS) is the most common valvular heart disease in developed countries.
no code implementations • 26 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.
no code implementations • 17 Jun 2024 • Jiyao Wang, Nicha C. Dvornek, Peiyu Duan, Lawrence H. Staib, Pamela Ventola, James S. Duncan
In recent years, graph neural networks (GNNs) have been widely applied in the analysis of brain fMRI, yet defining the connectivity between ROIs remains a challenge in noisy fMRI data.
no code implementations • 12 Jun 2024 • Tianqi Chen, Jun Hou, Yinchi Zhou, Huidong Xie, Xiongchao Chen, Qiong Liu, Xueqi Guo, Menghua Xia, James S. Duncan, Chi Liu, Bo Zhou
To address these challenges, we developed a novel 2. 5D Multi-view Averaging Diffusion Model (MADM) for 3D image-to-image translation with application on NAC-LDPET to AC-SDPET translation.
no code implementations • 23 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.
no code implementations • 6 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.
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.
no code implementations • 8 Mar 2024 • Daniel H. Pak, Minliang Liu, Theodore Kim, Caglar Ozturk, Raymond McKay, Ellen T. Roche, Rudolph Gleason, James S. Duncan
Calcification has significant influence over cardiovascular diseases and interventions.
no code implementations • 25 Jan 2024 • Bo Zhou, Jun Hou, Tianqi Chen, Yinchi Zhou, Xiongchao Chen, Huidong Xie, Qiong Liu, Xueqi Guo, Yu-Jung Tsai, Vladimir Y. Panin, Takuya Toyonaga, James S. Duncan, Chi Liu
Low-dose PET offers a valuable means of minimizing radiation exposure in PET imaging.
1 code implementation • 23 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.
no code implementations • 1 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.
no code implementations • 1 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.
no code implementations • 8 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.
no code implementations • 29 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.
no code implementations • 8 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.
no code implementations • 13 Apr 2023 • Peiyu Duan, Yuan Xue, Shuo Han, Lianrui Zuo, Aaron Carass, Caitlyn Bernhard, Savannah Hays, Peter A. Calabresi, Susan M. Resnick, James S. Duncan, Jerry L. Prince
The meninges, located between the skull and brain, are composed of three membrane layers: the pia, the arachnoid, and the dura.
1 code implementation • 6 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.
2 code implementations • 5 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.
1 code implementation • 2 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.
no code implementations • 2 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.
no code implementations • 18 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.
no code implementations • 14 Feb 2023 • Bo Zhou, Yu-Jung Tsai, Jiazhen Zhang, Xueqi Guo, Huidong Xie, Xiongchao Chen, Tianshun Miao, Yihuan Lu, James S. Duncan, Chi Liu
To this end, modeling-free universal motion correction reconstruction for accelerated PET is still highly under-explored.
1 code implementation • 27 Sep 2022 • Chenyu You, Weicheng Dai, Fenglin Liu, Yifei Min, Nicha C. Dvornek, 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.
1 code implementation • 20 Jul 2022 • Siyuan Dong, Gilbert Hangel, Eric Z. Chen, Shanhui Sun, Wolfgang Bogner, Georg Widhalm, Chenyu You, John A. Onofrey, Robin de Graaf, James S. Duncan
Specifically, we propose a flow-based enhancer network to improve the visual quality of super-resolution MRSI.
no code implementations • 7 Jun 2022 • Shanlin Sun, Kun Han, Chenyu You, Hao Tang, Deying Kong, Junayed Naushad, Xiangyi Yan, Haoyu Ma, Pooya Khosravi, James S. Duncan, Xiaohui Xie
Traditional methods for image registration are primarily optimization-driven, finding the optimal deformations that maximize the similarity between two images.
1 code implementation • 6 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.
no code implementations • 3 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.
no code implementations • 26 Jan 2022 • Bo Zhou, Neel Dey, Jo Schlemper, Seyed Sadegh Mohseni Salehi, Chi Liu, James S. Duncan, Michal Sofka
To these ends, we present a dual-domain self-supervised transformer (DSFormer) for accelerated MC-MRI reconstruction.
no code implementations • 26 Jan 2022 • Chenyu You, Ruihan Zhao, Fenglin Liu, Siyuan Dong, Sandeep Chinchali, Ufuk Topcu, Lawrence Staib, James S. Duncan
In this work, we present CASTformer, a novel type of adversarial transformers, for 2D medical image segmentation.
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.
no code implementations • 13 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.
1 code implementation • 12 Jul 2021 • Bo Zhou, Rui Wang, Ming-Kai Chen, Adam P. Mecca, Ryan S. O'Dell, Christopher H. Van Dyck, Richard E. Carson, James S. Duncan, Chi Liu
Thus, it is desirable to develop an efficient multi-tracer PET synthesis model that can generate multi-tracer PET from single-tracer PET.
1 code implementation • 12 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).
no code implementations • 14 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.
no code implementations • 6 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.
no code implementations • 15 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.
1 code implementation • 14 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.
1 code implementation • ICLR 2021 • Juntang Zhuang, Nicha C. Dvornek, Sekhar Tatikonda, James S. Duncan
Neural ordinary differential equations (Neural ODEs) are a new family of deep-learning models with continuous depth.
Ranked #24 on
Image Generation
on ImageNet 64x64
(Bits per dim metric)
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.
no code implementations • 6 Sep 2020 • Chenyu You, Junlin Yang, Julius Chapiro, James S. Duncan
However, the well-trained models often fail in the target domain due to the domain shift.
no code implementations • 3 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.
no code implementations • 2 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.
no code implementations • 29 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.
1 code implementation • 16 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.
no code implementations • 1 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.
no code implementations • 15 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.
1 code implementation • 30 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.
no code implementations • 25 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).
no code implementations • 27 Aug 2019 • Junlin Yang, Nicha C. Dvornek, Fan Zhang, Juntang Zhuang, Julius Chapiro, MingDe Lin, James S. Duncan
For the DA task, our DALACE model outperformed CycleGAN, TD-GAN , and DADR with DSC of 0. 847 compared to 0. 721, 0. 793 and 0. 806.
no code implementations • 31 Jul 2019 • Junlin Yang, Nicha C. Dvornek, Fan Zhang, Julius Chapiro, MingDe Lin, James S. Duncan
First, images from each domain are embedded into two spaces, a shared domain-invariant content space and a domain-specific style space.
1 code implementation • 23 Jul 2019 • Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li, Pamela Ventola, James S. Duncan
Recently deep learning methods have achieved success in the classification task of ASD using fMRI data.
no code implementations • 2 Jul 2019 • Xiaoxiao Li, Nicha C. Dvornek, Yuan Zhou, Juntang Zhuang, Pamela Ventola, James S. Duncan
Our pipeline can be generalized to other graph feature importance interpretation problems.
no code implementations • 14 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.
no code implementations • 23 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.
no code implementations • 12 Jul 2018 • Allen Lu, Nripesh Parajuli, Maria Zontak, John Stendahl, Kevinminh Ta, Zhao Liu, Nabil Boutagy, Geng-Shi Jeng, Imran Alkhalil, Lawrence H. Staib, Matthew O'Donnell, Albert J. Sinusas, James S. Duncan
We extended this framework to include a supervised loss term on synthetic data and showed the effects of biomechanical constraints on the network's ability for domain adaptation.
no code implementations • 9 Jul 2018 • Nripesh Parajuli, Allen Lu, Kevinminh Ta, John C. Stendahl, Nabil Boutagy, Imran Alkhalil, Melissa Eberle, Geng-Shi Jeng, Maria Zontak, Matthew ODonnell, Albert J. Sinusas, James S. Duncan
In this work, we propose a point matching scheme where correspondences are modeled as flow through a graphical network.
no code implementations • 24 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.
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.