Vishwesh Nath

7.7k total citations · 2 hit papers
50 papers, 2.6k citations indexed

About

Vishwesh Nath is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Vishwesh Nath has authored 50 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Radiology, Nuclear Medicine and Imaging, 13 papers in Artificial Intelligence and 10 papers in Computer Vision and Pattern Recognition. Recurrent topics in Vishwesh Nath's work include Advanced Neuroimaging Techniques and Applications (28 papers), Advanced MRI Techniques and Applications (25 papers) and MRI in cancer diagnosis (16 papers). Vishwesh Nath is often cited by papers focused on Advanced Neuroimaging Techniques and Applications (28 papers), Advanced MRI Techniques and Applications (25 papers) and MRI in cancer diagnosis (16 papers). Vishwesh Nath collaborates with scholars based in United States, India and Canada. Vishwesh Nath's co-authors include Bennett A. Landman, Daguang Xu, Holger R. Roth, Dong Yang, Yucheng Tang, Ali Hatamizadeh, Andriy Myronenko, Wenqi Li, Kurt G. Schilling and Adam W. Anderson and has published in prestigious journals such as Magnetic Resonance in Medicine, IEEE Transactions on Medical Imaging and Medical Image Analysis.

In The Last Decade

Vishwesh Nath

48 papers receiving 2.5k citations

Hit Papers

UNETR: Transformers for 3D Medical Image Segmentation 2022 2026 2023 2024 2022 2022 400 800 1.2k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. UNETR: Transformers for 3D Medical Image Segmentation
    2022 1.4k citations Yucheng Tang, Vishwesh Nath et al. profile →
  2. Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis
    2022 372 citations Yucheng Tang, Dong Yang et al. profile →

Peers

Vishwesh Nath
Christian Desrosiers Canada
Chunfeng Lian United States
Tal Arbel Canada
Ender Konukoğlu Switzerland
Christian Wachinger Germany
María A. Zuluaga France
Zhong Xue United States
Yuankai Huo United States
Evangelia I. Zacharaki Greece
Dong Nie United States
Christian Desrosiers Canada
Vishwesh Nath
Citations per year, relative to Vishwesh Nath Vishwesh Nath (= 1×) peers Christian Desrosiers

Countries citing papers authored by Vishwesh Nath

Since Specialization
Citations

This map shows the geographic impact of Vishwesh Nath's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Vishwesh Nath with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vishwesh Nath more than expected).

Fields of papers citing papers by Vishwesh Nath

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Vishwesh Nath. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Vishwesh Nath. The network helps show where Vishwesh Nath may publish in the future.

Co-authorship network of co-authors of Vishwesh Nath

This figure shows the co-authorship network connecting the top 25 collaborators of Vishwesh Nath. A scholar is included among the top collaborators of Vishwesh Nath based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Vishwesh Nath. Vishwesh Nath is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
He, Yufan, Yucheng Tang, Andriy Myronenko, et al.. (2025). VISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imaging. 20863–20873. 7 indexed citations
2.
Diaz‐Pinto, Andres, Vishwesh Nath, Yucheng Tang, et al.. (2024). MONAI Label: A framework for AI-assisted interactive labeling of 3D medical images. Medical Image Analysis. 95. 103207–103207. 36 indexed citations
3.
Nath, Vishwesh, Joselyn Rwebembera, Emmy Okello, et al.. (2024). Uncertainty-Driven Rheumatic Heart Disease Detection through Active Learning. 1–5.
4.
Yao, Tianyuan, François Rheault, Leon Y. Cai, et al.. (2023). Deep constrained spherical deconvolution for robust harmonization. PubMed. 12464. 27–27. 2 indexed citations
5.
Yao, Tianyuan, Vishwesh Nath, Leon Y. Cai, et al.. (2023). A Unified Learning Model for Estimating Fiber Orientation Distribution Functions on Heterogeneous Multi-shell Diffusion-Weighted MRI. Lecture notes in computer science. 14328. 13–22. 2 indexed citations
6.
Pathak, Sudhir, Vishwesh Nath, Walter Schneider, et al.. (2022). VRfRNet: Volumetric ROI fODF reconstruction network for estimation of multi-tissue constrained spherical deconvolution with only single shell dMRI. Magnetic Resonance Imaging. 90. 1–16. 8 indexed citations
7.
Cai, Leon Y., Qi Yang, Colin B. Hansen, et al.. (2021). PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images. Magnetic Resonance in Medicine. 86(1). 456–470. 63 indexed citations
8.
Nath, Vishwesh, Dong Yang, Bennett A. Landman, Daguang Xu, & Holger R. Roth. (2020). Diminishing Uncertainty Within the Training Pool: Active Learning for Medical Image Segmentation. IEEE Transactions on Medical Imaging. 40(10). 2534–2547. 49 indexed citations
9.
Hansen, Colin B., Baxter P. Rogers, Kurt G. Schilling, et al.. (2020). Empirical field mapping for gradient nonlinearity correction of multi-site diffusion weighted MRI. Magnetic Resonance Imaging. 76. 69–78. 15 indexed citations
10.
Schilling, Kurt G., Justin A. Blaber, Yuankai Huo, et al.. (2019). Synthesized b0 for diffusion distortion correction (Synb0-DisCo). Magnetic Resonance Imaging. 64. 62–70. 112 indexed citations
11.
Hansen, Colin B., Vishwesh Nath, Kurt G. Schilling, et al.. (2019). Consideration of cerebrospinal fluid intensity variation in diffusion weighted MRI. PubMed. 10948. 87–87. 2 indexed citations
12.
Nath, Vishwesh, Prasanna Parvathaneni, Kurt G. Schilling, et al.. (2019). A deep learning approach to estimation of subject-level bias and variance in high angular resolution diffusion imaging. Magnetic Resonance Imaging. 59. 130–136. 1 indexed citations
13.
Parvathaneni, Prasanna, Ilwoo Lyu, Yuankai Huo, et al.. (2019). Improved gray matter surface based spatial statistics in neuroimaging studies. Magnetic Resonance Imaging. 61. 285–295. 7 indexed citations
14.
Roy, Snehashis, Justin A. Blaber, Camilo Bermudez, et al.. (2019). Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury. PubMed. 10949. 9–9. 11 indexed citations
15.
Parvathaneni, Prasanna, Vishwesh Nath, Maureen McHugo, et al.. (2019). Improving human cortical sulcal curve labeling in large scale cross-sectional MRI using deep neural networks. Journal of Neuroscience Methods. 324. 108311–108311. 5 indexed citations
16.
Nath, Vishwesh, Kurt G. Schilling, Yurui Gao, et al.. (2019). Learning 3D White Matter Microstructure from 2D Histology. PubMed. 19. 186–190. 1 indexed citations
17.
Nath, Vishwesh, Kurt G. Schilling, Prasanna Parvathaneni, et al.. (2019). Deep learning reveals untapped information for local white-matter fiber reconstruction in diffusion-weighted MRI. Magnetic Resonance Imaging. 62. 220–227. 23 indexed citations
18.
Huo, Yuankai, Justin A. Blaber, Stephen Damon, et al.. (2018). Towards Portable Large-Scale Image Processing with High-Performance Computing. Journal of Digital Imaging. 31(3). 304–314. 19 indexed citations
19.
Schilling, Kurt G., Alessandro Daducci, Klaus Maier‐Hein, et al.. (2018). Challenges in diffusion MRI tractography – Lessons learned from international benchmark competitions. Magnetic Resonance Imaging. 57. 194–209. 88 indexed citations
20.
Nath, Vishwesh, et al.. (2017). Analysis of Quantum Algorithms with Classical Systems Counterpart. International Journal of Information Engineering and Electronic Business. 9(2). 20–26. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Christian Desrosiers Breakdown of academic impact, for papers by Chunfeng Lian Breakdown of academic impact, for papers by Tal Arbel Breakdown of academic impact, for papers by Ender Konukoğlu Breakdown of academic impact, for papers by Christian Wachinger Breakdown of academic impact, for papers by María A. Zuluaga Breakdown of academic impact, for papers by Zhong Xue Breakdown of academic impact, for papers by Yuankai Huo Breakdown of academic impact, for papers by Evangelia I. Zacharaki Breakdown of academic impact, for papers by Dong Nie
Rankless by CCL
2026