World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
65
Citations
23112
World Ranking
2414
National Ranking
1207

Overview

Bennett A. Landman is affiliated with Vanderbilt University in the United States. Their research primarily spans the domain of Medicine, with a significant focus on Radiology, Nuclear Medicine and Imaging. Specialized subfields of study also include Cognitive Neuroscience, Artificial Intelligence, Computer Vision and Pattern Recognition, and Pulmonary and Respiratory Medicine.

The scientist's work covers several main topics including Advanced Neuroimaging Techniques and Applications, Advanced MRI Techniques and Applications, Functional Brain Connectivity Studies, Radiomics and Machine Learning in Medical Imaging, MRI in cancer diagnosis, AI in cancer detection, and Advanced Neural Network Applications.

Frequent collaborators in Bennett A. Landman's research include Kurt G. Schilling, Yuankai Huo, Shunxing Bao, Timothy J. Hohman, and Derek B. Archer.

Publication venues where Bennett A. Landman has contributed extensively feature:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Alzheimer's & Dementia
  • Journal of Medical Imaging
  • Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition

Bennett A. Landman has co-authored numerous papers, including recent publications such as:

  • UNETR: Transformers for 3D Medical Image Segmentation (2022), presented at the 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • The Medical Segmentation Decathlon (2022), published in Nature Communications
  • Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis (2022), presented at the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Metrics reloaded: recommendations for image analysis validation (2024), published in Nature Methods
  • Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives (2023), published in Medical Image Analysis

In addition to journal articles and conference papers, Bennett A. Landman has contributed to book publications under Springer Science+Business Media. Titles include:

  • Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning (2020)
  • Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning (2021)

Best Publications

  • The future of digital health with federated learning

    Nicola Rieke;Nicola Rieke;Jonny Hancox;Wenqi Li;Fausto Milletari

  • The Medical Segmentation Decathlon

    Michela Antonelli;Annika Reinke;Spyridon Bakas;Keyvan Farahani

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

    Unknown

  • A large annotated medical image dataset for the development and evaluation of segmentation algorithms

    Amber L. Simpson;Michela Antonelli;Spyridon Bakas;Michel Bilello

  • Water saturation shift referencing (WASSR) for chemical exchange saturation transfer (CEST) experiments.

    Mina Kim;Mina Kim;Joseph Gillen;Joseph Gillen;Bennett A. Landman;Jinyuan Zhou;Jinyuan Zhou

  • Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA-DTI working group

    Neda Jahanshad;Peter V. Kochunov;Emma Sprooten;Emma Sprooten;René C. Mandl

  • Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T.

    Bennett A. Landman;Jonathan A.D. Farrell;Jonathan A.D. Farrell;Craig K. Jones;Craig K. Jones;Seth A. Smith;Seth A. Smith

  • Federated learning enables big data for rare cancer boundary detection

    Unknown

  • SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth

    Yuankai Huo;Zhoubing Xu;Hyeonsoo Moon;Shunxing Bao

  • Effects of signal-to-noise ratio on the accuracy and reproducibility of diffusion tensor imaging–derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T

    Jonathan A.D. Farrell;Bennett A. Landman;Craig K. Jones;Craig K. Jones;Seth A. Smith;Seth A. Smith

  • Why rankings of biomedical image analysis competitions should be interpreted with care

    Lena Maier-Hein;Matthias Eisenmann;Annika Reinke;Sinan Onogur

  • Multi-parametric neuroimaging reproducibility: a 3-T resource study.

    Bennett A. Landman;Bennett A. Landman;Alan J. Huang;Alan J. Huang;Aliya Gifford;Deepti S. Vikram;Deepti S. Vikram

  • 3D whole brain segmentation using spatially localized atlas network tiles

    Yuankai Huo;Zhoubing Xu;Yunxi Xiong;Katherine Aboud

  • Deep learning for brain tumor classification

    Justin S. Paul;Andrew J. Plassard;Bennett A. Landman;Daniel Fabbri

  • Non-local statistical label fusion for multi-atlas segmentation

    Andrew J. Asman;Bennett A. Landman

  • Limits to anatomical accuracy of diffusion tractography using modern approaches.

    Kurt G. Schilling;Vishwesh Nath;Colin Hansen;Prasanna Parvathaneni

  • Heritability of fractional anisotropy in human white matter: A comparison of Human Connectome Project and ENIGMA-DTI data

    Peter Kochunov;Neda Jahanshad;Daniel Marcus;Anderson Winkler

  • Histological validation of diffusion MRI fiber orientation distributions and dispersion.

    Kurt G. Schilling;Vaibhav A. Janve;Yurui Gao;Iwona Stepniewska

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

    Kurt G. Schilling;Justin Blaber;Yuankai Huo;Allen Newton

  • Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking.

    Mengyang Zhao;Aadarsh Jha;Quan Liu;Bryan A. Millis

  • Evaluation of Six Registration Methods for the Human Abdomen on Clinically Acquired CT

    Zhoubing Xu;Christopher P. Lee;Mattias P. Heinrich;Marc Modat

  • The Java Image Science Toolkit (JIST) for Rapid Prototyping and Publishing of Neuroimaging Software

    Blake C. Lucas;Blake C. Lucas;John A. Bogovic;Aaron Carass;Pierre Louis Bazin

  • Resolution of crossing fibers with constrained compressed sensing using diffusion tensor MRI

    Bennett A. Landman;Bennett A. Landman;John A. Bogovic;Hanlin Wan;Fatma El Zahraa ElShahaby

  • Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA-DTI working group

    N. Jahanshad;P. V. Kochunov;E. Sprooten;R. C. Mandl

Frequent Co-Authors

Yuankai Huo
Yuankai Huo Vanderbilt University
Susan M. Resnick
Susan M. Resnick National Institutes of Health
Jerry L. Prince
Jerry L. Prince Johns Hopkins University
Baxter P. Rogers
Baxter P. Rogers Vanderbilt University
Neil D. Woodward
Neil D. Woodward Vanderbilt University Medical Center
Iwona Stepniewska
Iwona Stepniewska Vanderbilt University
Warren D. Taylor
Warren D. Taylor Vanderbilt University Medical Center
Lori L. Beason-Held
Lori L. Beason-Held National Institutes of Health
David H. Zald
David H. Zald Rutgers, The State University of New Jersey
Laurie E. Cutting
Laurie E. Cutting Vanderbilt University

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