Search Results for author: Vishwesh Nath

Found 22 papers, 7 papers with code

COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation

1 code implementation22 Jul 2023 Han Liu, Hao Li, Xing Yao, Yubo Fan, Dewei Hu, Benoit Dawant, Vishwesh Nath, Zhoubing Xu, Ipek Oguz

Cold-start AL is highly relevant in many practical scenarios but has been under-explored, especially for 3D medical segmentation tasks requiring substantial annotation effort.

Active Learning Image Segmentation +3

Fair Federated Medical Image Segmentation via Client Contribution Estimation

no code implementations CVPR 2023 Meirui Jiang, Holger R Roth, Wenqi Li, Dong Yang, Can Zhao, Vishwesh Nath, Daguang Xu, Qi Dou, Ziyue Xu

Recent studies have investigated how to reward clients based on their contribution (collaboration fairness), and how to achieve uniformity of performance across clients (performance fairness).

Fairness Federated Learning +3

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

no code implementations29 Mar 2023 Tianyuan Yao, Nancy Newlin, Praitayini Kanakaraj, Vishwesh Nath, Leon Y Cai, Karthik Ramadass, Kurt Schilling, Bennett A. Landman, Yuankai Huo

Diffusion-weighted (DW) MRI measures the direction and scale of the local diffusion process in every voxel through its spectrum in q-space, typically acquired in one or more shells.

Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples

no code implementations ICCV 2023 Jingwei Sun, Ziyue Xu, Dong Yang, Vishwesh Nath, Wenqi Li, Can Zhao, Daguang Xu, Yiran Chen, Holger R. Roth

We propose a practical vertical federated learning (VFL) framework called \textbf{one-shot VFL} that can solve the communication bottleneck and the problem of limited overlapping samples simultaneously based on semi-supervised learning.

Vertical Federated Learning

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

2 code implementations4 Jan 2022 Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang, Dong Yang, Holger Roth, Daguang Xu

Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient and successively studying the progression of the malignant entity.

3D Semantic Segmentation Brain Tumor Segmentation +2

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

1 code implementation CVPR 2022 Yucheng Tang, Dong Yang, Wenqi Li, Holger Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, Ali Hatamizadeh

Vision Transformers (ViT)s have shown great performance in self-supervised learning of global and local representations that can be transferred to downstream applications.

 Ranked #1 on Medical Image Segmentation on Synapse multi-organ CT (using extra training data)

Anatomy Computed Tomography (CT) +3

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

no code implementations12 Jul 2021 Vishwesh Nath, Dong Yang, Ali Hatamizadeh, Anas A. Abidin, Andriy Myronenko, Holger Roth, Daguang Xu

First, we show higher correlation to using full data for training when testing on the external validation set using smaller proxy data than a random selection of the proxy data.

AutoML Image Segmentation +3

UNETR: Transformers for 3D Medical Image Segmentation

10 code implementations18 Mar 2021 Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath, Dong Yang, Andriy Myronenko, Bennett Landman, Holger Roth, Daguang Xu

Inspired by the recent success of transformers for Natural Language Processing (NLP) in long-range sequence learning, we reformulate the task of volumetric (3D) medical image segmentation as a sequence-to-sequence prediction problem.

3D Medical Imaging Segmentation Image Segmentation +3

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

no code implementations7 Jan 2021 Vishwesh Nath, Dong Yang, Bennett A. Landman, Daguang Xu, Holger R. Roth

The primary advantage being that active learning frameworks select data points that can accelerate the learning process of a model and can reduce the amount of data needed to achieve full accuracy as compared to a model trained on a randomly acquired data set.

Active Learning BIG-bench Machine Learning +4

The Value of Nullspace Tuning Using Partial Label Information

no code implementations17 Mar 2020 Colin B. Hansen, Vishwesh Nath, Diego A. Mesa, Yuankai Huo, Bennett A. Landman, Thomas A. Lasko

But in some learning problems, partial label information can be inferred from otherwise unlabeled examples and used to further improve the model.

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

no code implementations20 Feb 2020 Vishwesh Nath, Sudhir K. Pathak, Kurt G. Schilling, Walt Schneider, Bennett A. Landman

Herein, we explore the possibility of using deep learning on single shell data (using the b=1000 s/mm2 from the Human Connectome Project (HCP)) to estimate the information content captured by 8th order MT-CSD using the full three shell data (b=1000, 2000, and 3000 s/mm2 from HCP).

Deep Learning Captures More Accurate Diffusion Fiber Orientations Distributions than Constrained Spherical Deconvolution

no code implementations13 Nov 2019 Vishwesh Nath, Kurt G. Schilling, Colin B. Hansen, Prasanna Parvathaneni, Allison E. Hainline, Camilo Bermudez, Andrew J. Plassard, Vaibhav Janve, Yurui Gao, Justin A. Blaber, Iwona Stępniewska, Adam W. Anderson, Bennett A. Landman

Confocal histology provides an opportunity to establish intra-voxel fiber orientation distributions that can be used to quantitatively assess the biological relevance of diffusion weighted MRI models, e. g., constrained spherical deconvolution (CSD).

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