V. Nath

发表

Diminishing Uncertainty Within the Training Pool: Active Learning for Medical Image Segmentation

Daguang Xu, Bennett A Landman, Vishwesh Nath, 2020, IEEE Transactions on Medical Imaging.

Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI.

Colin B. Hansen, Justin A. Blaber, Vaibhav A. Janve, 2019, Magnetic resonance imaging.

A deep learning approach to estimation of subject-level bias and variance in high angular resolution diffusion imaging.

Prasanna Parvathaneni, Bennett A Landman, Vishwesh Nath, 2019, Magnetic resonance imaging.

Empirical field mapping for gradient nonlinearity correction of multi-site diffusion weighted MRI

Carlo Pierpaoli, Baxter P. Rogers, Bennett A. Landman, 2020, bioRxiv.

Empirical single sample quantification of bias and variance in Q‐ball imaging

Justin A. Blaber, A. Anderson, B. Landman, 2018, Magnetic resonance in medicine.

Empirical Estimation of Intra-Voxel Structure with Persistent Angular Structure and Q-ball Models of Diffusion Weighted MRI

Vishwesh Nath, V. Nath, Nina Watson, 2022 .

MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging

Benjamin N. Conrad, Colin B. Hansen, Gavin R. Price, 2020, bioRxiv.

Body Part Regression With Self-Supervision

Shunxing Bao, Camilo Bermudez, Yuankai Huo, 2021, IEEE Transactions on Medical Imaging.

Characterization and correlation of signal drift in diffusion weighted MRI.

Colin B. Hansen, Justin A. Blaber, Roza G. Bayrak, 2019, Magnetic resonance imaging.

Empirical reproducibility, sensitivity, and optimization of acquisition protocol, for Neurite Orientation Dispersion and Density Imaging using AMICO.

Prasanna Parvathaneni, Bennett A Landman, Vishwesh Nath, 2018, Magnetic resonance imaging.

Computational Diffusion MRI

Vishwesh Nath, Marco Palombo, Marco Pizzolato, 2019, Mathematics and Visualization.

MONAI: An open-source framework for deep learning in healthcare

Mohammad Zalbagi Darestani, Paul F. Jaeger, Brad W. Genereaux, 2022, ArXiv.

Warm Start Active Learning with Proxy Labels and Selection via Semi-supervised Fine-Tuning

H. Roth, Daguang Xu, Dong Yang, 2022, MICCAI.

Limits to anatomical accuracy of diffusion tractography using modern approaches

Peter F. Neher, Prasanna Parvathaneni, Yonggang Shi, 2018, NeuroImage.

Synthesized b0 for diffusion distortion correction (Synb0-DisCo).

Yuankai Huo, Susan M Resnick, Bennett A Landman, 2019, Magnetic resonance imaging.

The Value of Nullspace Tuning Using Partial Label Information pdf

Bennett A. Landman, Vishwesh Nath, Yuankai Huo, 2020, ArXiv.

Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Holger Roth, Vishwesh Nath, Daguang Xu, 2022, BrainLes@MICCAI.

MASiVar: Multisite, multiscanner, and multisubject acquisitions for studying variability in diffusion weighted MRI

Benjamin N. Conrad, Colin B. Hansen, Cailey I. Kerley, 2021, Magnetic resonance in medicine.

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: Chronicles of the MEMENTO challenge

Derek K. Jones, R. Deriche, B. Jeurissen, 2021, NeuroImage.

DW-MRI Microstructure Model of Models Captured Via Single-Shell Bottleneck Deep Learning

Colin B. Hansen, A. Anderson, B. Landman, 2021, Computational Diffusion MRI.

MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging

Benjamin N. Conrad, Colin B. Hansen, Cailey I. Kerley, 2020, bioRxiv.

PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images

Benjamin N. Conrad, Colin B. Hansen, Brian D. Boyd, 2020, bioRxiv.

Deep Learning Estimation of Multi-Tissue Constrained Spherical Deconvolution with Limited Single Shell DW-MRI

Bennett A. Landman, Vishwesh Nath, Kurt G. Schilling, 2020, Medical Imaging: Image Processing.

Enabling Multi-Shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE

Yuankai Huo, Prasanna Parvathaneni, Ilwoo Lyu, 2019, MICCAI.

A fiber coherence index for quality control of B-table orientation in diffusion MRI scans.

Bennett A Landman, Fang-Cheng Yeh, Vishwesh Nath, 2019, Magnetic resonance imaging.

Harmonizing 1.5T/3T diffusion weighted MRI through development of deep learning stabilized microarchitecture estimators

Camilo Bermudez, Yuankai Huo, Prasanna Parvathaneni, 2019, Medical Imaging: Image Processing.

Inter-Scanner Harmonization of High Angular Resolution DW-MRI using Null Space Deep Learning pdf

Camilo Bermudez, Prasanna Parvathaneni, Ilwoo Lyu, 2018, Lecture notes-monograph series.

Tractography Reproducibility Challenge with Empirical Data (TraCED): The 2017 ISMRM Diffusion Study Group Challenge

Peter F. Neher, Arthur W. Toga, Ragini Verma, 2018, bioRxiv.

SHARD: spherical harmonic-based robust outlier detection for HARDI methods

Prasanna Parvathaneni, Ilwoo Lyu, Baxter P. Rogers, 2018, Medical Imaging.

Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

Christophe Lenglet, Vince D. Calhoun, Chun-Hung Yeh, 2020, NeuroImage.

Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis

B. Landman, H. Roth, Ali Hatamizadeh, 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Challenges in diffusion MRI tractography - Lessons learned from international benchmark competitions.

Bennett A Landman, Vishwesh Nath, Cyril Poupon, 2019, Magnetic resonance imaging.

Deep Learning Improves Pre-Surgical White Matter Visualization in Glioma Patients

S. Panesar, V. Nath, S. Pathak, 2020, medRxiv.

Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury

Snehashis Roy, Camilo Bermudez, Bennett A. Landman, 2019, Medical Imaging: Image Processing.

MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images

S. Ourselin, Fernando Pérez-García, M. Cardoso, 2022, ArXiv.

DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images

S. Ourselin, S. Pieper, M. Cardoso, 2023, DALI@MICCAI.

High-resolution 3D abdominal segmentation with random patch network fusion

Shunxing Bao, Camilo Bermudez, Yuankai Huo, 2020, Medical Image Anal..

HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet

Ramalingam Chellappa, A. Myronenko, Ali Hatamizadeh, 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Empirical field mapping for gradient nonlinearity correction of multi-site diffusion weighted MRI

Carlo Pierpaoli, Baxter P. Rogers, Bennett A. Landman, 2020, bioRxiv.

Comparison of multi-fiber reproducibility of PAS-MRI and Q-ball with empirical multiple b-value HARDI

Bennett A. Landman, Vishwesh Nath, Zhaohua Ding, 2017, Medical Imaging.

Robust Fiber ODF Estimation Using Deep Constrained Spherical Deconvolution for Diffusion MRI

S. Resnick, K. Schilling, Yuankai Huo, 2023, ArXiv.

Effects of b-value and number of gradient directions on diffusion MRI measures obtained with Q-ball imaging

Bennett A. Landman, Vishwesh Nath, Zhaohua Ding, 2017, Medical Imaging.

Towards Portable Large-Scale Image Processing with High-Performance Computing

Shunxing Bao, Prasanna Parvathaneni, Ilwoo Lyu, 2018, Journal of Digital Imaging.

Fair Federated Medical Image Segmentation via Client Contribution Estimation

Ziyue Xu, H. Roth, Daguang Xu, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Improving human cortical sulcal curve labeling in large scale cross-sectional MRI using deep neural networks

Yuankai Huo, Prasanna Parvathaneni, Maureen McHugo, 2019, Journal of Neuroscience Methods.

VRfRNet: Volumetric ROI fODF reconstruction network for estimation of multi-tissue constrained spherical deconvolution with only single shell dMRI.

Ranjeet Ranjan Jha, W. Schneider, A. Bhavsar, 2022, Magnetic resonance imaging.

Deep constrained spherical deconvolution for robust harmonization

K. Schilling, Yuankai Huo, F. Rheault, 2023, Medical Imaging.

Improved gray matter surface based spatial statistics in neuroimaging studies.

A. Anderson, Ilwoo Lyu, B. Landman, 2019, Magnetic resonance imaging.

Cortical Surface Parcellation Using Spherical Convolutional Neural Networks

Shunxing Bao, Yuankai Huo, Prasanna Parvathaneni, 2019, MICCAI.

The Power of Proxy Data and Proxy Networks for Hyper-Parameter Optimization in Medical Image Segmentation pdf

Daguang Xu, Holger Roth, Vishwesh Nath, 2021, MICCAI.

A Unified Single-stage Learning Model for Estimating Fiber Orientation Distribution Functions on Heterogeneous Multi-shell Diffusion-weighted MRI

K. Schilling, Yuankai Huo, Karthik Ramadass, 2023, ArXiv.

Empirical consideration of the effects of acquisition parameters and analysis model on clinically feasible q-ball imaging.

Prasanna Parvathaneni, Bennett A Landman, Vishwesh Nath, 2017, Magnetic resonance imaging.

Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples

Ziyue Xu, H. Roth, Daguang Xu, 2023, 2023 IEEE/CVF International Conference on Computer Vision (ICCV).

Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training

Vishal M. Patel, Jeya Maria Jose Valanarasu, Ziyue Xu, 2023, ArXiv.

VISTA3D: Versatile Imaging SegmenTation and Annotation model for 3D Computed Tomography

B. Turkbey, A. Myronenko, Daguang Xu, 2024, 2406.05285.