no code implementations • 28 May 2024 • Quan Liu, Ruining Deng, Can Cui, Tianyuan Yao, Vishwesh Nath, Yucheng Tang, Yuankai Huo
Multi-modal learning adeptly integrates visual and textual data, but its application to histopathology image and text analysis remains challenging, particularly with large, high-resolution images like gigapixel Whole Slide Images (WSIs).
1 code implementation • 31 Jul 2023 • Jeya Maria Jose Valanarasu, Yucheng Tang, Dong Yang, Ziyue Xu, Can Zhao, Wenqi Li, Vishal M. Patel, Bennett Landman, Daguang Xu, Yufan He, Vishwesh Nath
We curate a large-scale dataset to enable pre-training of 3D medical radiology images (MRI and CT).
1 code implementation • 22 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.
no code implementations • 5 Jun 2023 • Tianyuan Yao, Francois Rheault, Leon Y Cai, Vishwesh Nath, Zuhayr Asad, Nancy Newlin, Can Cui, Ruining Deng, Karthik Ramadass, Andrea Shafer, Susan Resnick, Kurt Schilling, Bennett A. Landman, Yuankai Huo
From the experimental results, the proposed data-driven framework outperforms the existing benchmarks in repeated fODF estimation.
1 code implementation • 18 May 2023 • Andres Diaz-Pinto, Pritesh Mehta, Sachidanand Alle, Muhammad Asad, Richard Brown, Vishwesh Nath, Alvin Ihsani, Michela Antonelli, Daniel Palkovics, Csaba Pinter, Ron Alkalay, Steve Pieper, Holger R. Roth, Daguang Xu, Prerna Dogra, Tom Vercauteren, Andrew Feng, Abood Quraini, Sebastien Ourselin, M. Jorge Cardoso
Automatic segmentation of medical images is a key step for diagnostic and interventional tasks.
no code implementations • 29 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.
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).
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.
1 code implementation • 4 Nov 2022 • M. Jorge Cardoso, Wenqi Li, Richard Brown, Nic Ma, Eric Kerfoot, Yiheng Wang, Benjamin Murrey, Can Zhao, Dong Yang, Vishwesh Nath, Yufan He, Ziyue Xu, Ali Hatamizadeh, Andriy Myronenko, Wentao Zhu, Yun Liu, Mingxin Zheng, Yucheng Tang, Isaac Yang, Michael Zephyr, Behrooz Hashemian, Sachidanand Alle, Mohammad Zalbagi Darestani, Charlie Budd, Marc Modat, Tom Vercauteren, Guotai Wang, Yiwen Li, Yipeng Hu, Yunguan Fu, Benjamin Gorman, Hans Johnson, Brad Genereaux, Barbaros S. Erdal, Vikash Gupta, Andres Diaz-Pinto, Andre Dourson, Lena Maier-Hein, Paul F. Jaeger, Michael Baumgartner, Jayashree Kalpathy-Cramer, Mona Flores, Justin Kirby, Lee A. D. Cooper, Holger R. Roth, Daguang Xu, David Bericat, Ralf Floca, S. Kevin Zhou, Haris Shuaib, Keyvan Farahani, Klaus H. Maier-Hein, Stephen Aylward, Prerna Dogra, Sebastien Ourselin, Andrew Feng
For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e. g. geometry, physiology, physics) of medical data being processed.
no code implementations • 13 Sep 2022 • Vishwesh Nath, Dong Yang, Holger R. Roth, Daguang Xu
Which volume to annotate next is a challenging problem in building medical imaging datasets for deep learning.
2 code implementations • 23 Mar 2022 • Andres Diaz-Pinto, Sachidanand Alle, Vishwesh Nath, Yucheng Tang, Alvin Ihsani, Muhammad Asad, Fernando Pérez-García, Pritesh Mehta, Wenqi Li, Mona Flores, Holger R. Roth, Tom Vercauteren, Daguang Xu, Prerna Dogra, Sebastien Ourselin, Andrew Feng, M. Jorge Cardoso
MONAI Label allows researchers to make incremental improvements to their AI-based annotation application by making them available to other researchers and clinicians alike.
3 code implementations • 4 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.
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)
no code implementations • 12 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.
10 code implementations • 18 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.
no code implementations • 7 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.
no code implementations • 17 Mar 2020 • Colin B. Hansen, Vishwesh Nath, Diego A. Mesa, Yuankai Huo, Bennett A. Landman, Thomas A. Lasko
In some learning problems, partial label information can be inferred from otherwise unlabeled examples and used to further improve the model.
no code implementations • 20 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).
no code implementations • 13 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).
no code implementations • 15 Jul 2019 • Vishwesh Nath, Ilwoo Lyu, Kurt G. Schilling, Prasanna Parvathaneni, Colin B. Hansen, Yucheng Tang, Yuankai Huo, Vaibhav A. Janve, Yurui Gao, Iwona Stepniewska, Adam W. Anderson, Bennett A. Landman
In the in-vivo human data, Deep SHORE was more consistent across scanners with 0. 63 relative to other multi-shell methods 0. 39, 0. 52 and 0. 57 in terms of ACC.
no code implementations • 11 Mar 2019 • Samuel Remedios, Snehashis Roy, Justin Blaber, Camilo Bermudez, Vishwesh Nath, Mayur B. Patel, John A. Butman, Bennett A. Landman, Dzung L. Pham
Machine learning models are becoming commonplace in the domain of medical imaging, and with these methods comes an ever-increasing need for more data.
no code implementations • 10 Nov 2018 • Yuankai Huo, James G. Terry, Jiachen Wang, Vishwesh Nath, Camilo Bermudez, Shunxing Bao, Prasanna Parvathaneni, J. Jeffery Carr, Bennett A. Landman
From the results, the proposed AID-Net achieved the superior performance on classification accuracy (0. 9272) and AUC (0. 9627).
no code implementations • 9 Oct 2018 • Vishwesh Nath, Prasanna Parvathaneni, Colin B. Hansen, Allison E. Hainline, Camilo Bermudez, Samuel Remedios, Justin A. Blaber, Kurt G. Schilling, Ilwoo Lyu, Vaibhav Janve, Yurui Gao, Iwona Stepniewska, Baxter P. Rogers, Allen T. Newton, L. Taylor Davis, Jeff Luci, Adam W. Anderson, Bennett A. Landman
Herein, we propose a data-driven tech-nique using a neural network design which exploits two categories of data.