Landman, Bennett A.

The future of digital health with federated learning

Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine

Privacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and Education

Cited by 4,015

UNETR: Transformers for 3D Medical Image Segmentation

Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H. R., & Xu, D. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 3,586

UNETR: Transformers for 3D Medical Image Segmentation

Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H., & Xu, D. (2021) · arXiv

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 3,580

The Future of Digital Health with Federated Learning

Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B., Maier-Hein, K., et al. (2020) · arXiv

Privacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and Education

Cited by 3,495

The Medical Segmentation Decathlon

Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B. A., Litjens, G., Menze, B., Ronneberger, O., Summers, R. M., et al. (2022) · Nature Communications

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 1,384

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

Tang, Y., Yang, D., Li, W., Roth, H. R., Landman, B., Xu, D., Nath, V., & Hatamizadeh, A. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Medical Image Segmentation TechniquesDomain Adaptation and Few-Shot Learning

Cited by 849

The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data

the Alzheimer’s Disease Neuroimaging Initiative, E. C., Thompson, P. M., Stein, J. L., Medland, S. E., Hibar, D. P., Vasquez, A. A., Renteria, M. E., Toro, R., Jahanshad, N., Schumann, G., et al. (2014) · Brain Imaging and Behavior

Genetic Associations and EpidemiologyFunctional Brain Connectivity Studies

Cited by 838

Federated learning enables big data for rare cancer boundary detection

Pati, S., Baid, U., Edwards, B., Sheller, M., Wang, S.-H., Reina, G. A., Foley, P., Gruzdev, A., Karkada, D., Davatzikos, C., et al. (2022) · Nature Communications

Privacy-Preserving Technologies in DataBrain Tumor Detection and Classification

Cited by 394

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

Jahanshad, N., Kochunov, P. V., Sprooten, E., Mandl, R. C., Nichols, T. E., Almasy, L., Blangero, J., Brouwer, R. M., Curran, J. E., de Zubicaray, G. I., et al. (2013) · NeuroImage

Advanced Neuroimaging Techniques and ApplicationsGenetic Associations and Epidemiology

Cited by 393

Limits to anatomical accuracy of diffusion tractography using modern approaches

Schilling, K. G., Nath, V., Hansen, C., Parvathaneni, P., Blaber, J., Gao, Y., Neher, P., Aydogan, D. B., Shi, Y., Ocampo-Pineda, M., et al. (2019) · NeuroImage

Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity Studies

Cited by 260

Understanding metric-related pitfalls in image analysis validation

Reinke, A., Tizabi, M. D., Baumgartner, M., Eisenmann, M., Heckmann-Nötzel, D., Kavur, A. E., Rädsch, T., Sudre, C. H., Acion, L., Antonelli, M., et al. (2024) · Nature Methods

Cell Image Analysis TechniquesMedical Image Segmentation Techniques

Cited by 241

3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation

Lee, H. H., Bao, S., Huo, Y., & Landman, B. A. (2022) · arXiv

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 225

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

Kochunov, P., Jahanshad, N., Marcus, D., Winkler, A., Sprooten, E., Nichols, T. E., Wright, S. N., Hong, L. E., Patel, B., Behrens, T., et al. (2015) · NeuroImage

Advanced Neuroimaging Techniques and ApplicationsGenetic Associations and Epidemiology

Cited by 215

Histological validation of diffusion MRI fiber orientation distributions and dispersion

Schilling, K. G., Janve, V., Gao, Y., Stepniewska, I., Landman, B. A., & Anderson, A. W. (2018) · NeuroImage

Advanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and Applications

Cited by 195

Assessing the accuracy of a deep learning method to risk stratify indeterminate pulmonary nodules

Massion, P. P., Antic, S., Ather, S., Arteta, C., Brabec, J., Chen, H., Declerck, J., Dufek, D., Hickes, W., Kadir, T., et al. (2020) · American Journal of Respiratory and Critical Care Medicine

Lung Cancer Diagnosis and TreatmentAI in cancer detection

Cited by 190

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

Schilling, K. G., Rheault, F., Petit, L., Hansen, C. B., Nath, V., Yeh, F.-C., Girard, G., Barakovic, M., Rafael-Patino, J., Yu, T., et al. (2021) · NeuroImage

Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity Studies

Cited by 182

Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling

Kochunov, P., Jahanshad, N., Sprooten, E., Nichols, T. E., Mandl, R. C., Almasy, L., Booth, T., Brouwer, R. M., Curran, J. E., de Zubicaray, G. I., et al. (2014) · NeuroImage

Advanced Neuroimaging Techniques and ApplicationsGenetic Associations and Epidemiology

Cited by 133

Common Limitations of Image Processing Metrics: A Picture Story

Reinke, A., Tizabi, M. D., Sudre, C. H., Eisenmann, M., Rädsch, T., Baumgartner, M., Acion, L., Antonelli, M., Arbel, T., Bakas, S., et al. (2021) · arXiv

Medical Image Segmentation TechniquesCell Image Analysis Techniques

Cited by 126

Can increased spatial resolution solve the crossing fiber problem for diffusion MRI?

Schilling, K., Gao, Y., Janve, V., Stepniewska, I., Landman, B. A., & Anderson, A. W. (2017) · NMR in Biomedicine

Advanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and Applications

Cited by 102

Cross-scale multi-instance learning for pathological image diagnosis

Deng, R., Cui, C., Remedios, L. W., Bao, S., Womick, R. M., Chiron, S., Li, J., Roland, J. T., Lau, K. S., Liu, Q., et al. (2024) · Medical Image Analysis

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 95

Aging and white matter microstructure and macrostructure: A longitudinal multi-site diffusion MRI study of 1218 participants

Schilling, K. G., Archer, D., Yeh, F.-C., Rheault, F., Cai, L. Y., Hansen, C., Yang, Q., Ramdass, K., Shafer, A. T., Resnick, S. M., et al. (2022) · Brain Structure and Function

Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity Studies

Cited by 78

White matter abnormalities and cognition in aging and Alzheimer disease

Peter, C., Sathe, A., Shashikumar, N., Pechman, K. R., Workmeister, A. W., Jackson, T. B., Huo, Y., Mukherjee, S., Mez, J., Dumitrescu, L. C., et al. (2025) · JAMA Neurology

Dementia and Cognitive Impairment ResearchAdvanced Neuroimaging Techniques and Applications

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Metrics reloaded: Recommendations for image analysis validation

Maier-Hein, L., Reinke, A., Godau, P., Tizabi, M. D., Buettner, F., Christodoulou, E., Glocker, B., Isensee, F., Kleesiek, J., Kozubek, M., et al. (2022) · arXiv

Cell Image Analysis TechniquesMedical Image Segmentation Techniques

Cited by 59

Prenatal and postnatal maternal anxiety and amygdala structure and function in young children

Donnici, C., Long, X., Dewey, D., Letourneau, N., Landman, B., Huo, Y., & Lebel, C. (2021) · Scientific Reports

Maternal Mental Health During Pregnancy and PostpartumChild and Adolescent Psychosocial and Emotional Development

Cited by 54