Roth, Holger R.

Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning

Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging

AI in cancer detectionAdvanced Neural Network Applications

Cited by 5,759

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

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

Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H. R., & Xu, D. (2022) · Lecture Notes in Computer Science

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 2,273

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

Federated learning for predicting clinical outcomes in patients with COVID-19

Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C.-S., et al. (2021) · Nature Medicine

Machine Learning in HealthcareCOVID-19 diagnosis using AI

Cited by 842

DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

Roth, H. R., Lu, L., Farag, A., Shin, H.-C., Liu, J., Turkbey, E. B., & Summers, R. M. (2015) · Lecture Notes in Computer Science

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 784

Improving Computer-Aided Detection UsingConvolutional Neural Networks and Random View Aggregation

Roth, H. R., Lu, L., Liu, J., Yao, J., Seff, A., Cherry, K., Kim, L., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 608

Artificial intelligence for the detection of COVID-19 pneumonia on chest ct using multinational datasets

Harmon, S. A., Sanford, T. H., Xu, S., Turkbey, E. B., Roth, H., Xu, Z., Yang, D., Myronenko, A., Anderson, V., Amalou, A., et al. (2020) · Nature Communications

COVID-19 diagnosis using AIArtificial Intelligence in Healthcare and Education

Cited by 587

Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation

Roth, H. R., Lu, L., Lay, N., Harrison, A. P., Farag, A., Sohn, A., & Summers, R. M. (2018) · Medical Image Analysis

Medical Image Segmentation TechniquesMedical Imaging and Analysis

Cited by 325

Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan

Yang, D., Xu, Z., Li, W., Myronenko, A., Roth, H. R., Harmon, S., Xu, S., Turkbey, B., Turkbey, E., Wang, X., et al. (2021) · Medical Image Analysis

COVID-19 diagnosis using AIDomain Adaptation and Few-Shot Learning

Cited by 279

Federated learning improves site performance in multicenter deep learning without data sharing

Sarma, K. V., Harmon, S., Sanford, T., Roth, H. R., Xu, Z., Tetreault, J., Xu, D., Flores, M. G., Raman, A. G., Kulkarni, R., et al. (2021) · Journal of the American Medical Informatics Association

Privacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and Education

Cited by 242

Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation

Liu, K.-L., Wu, T., Chen, P.-T., Tsai, Y. M., Roth, H., Wu, M.-S., Liao, W.-C., & Wang, W. (2020) · The Lancet Digital Health

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 222

Federated Learning for Breast Density Classification: A Real-World Implementation

Roth, H. R., Chang, K., Singh, P., Neumark, N., Li, W., Gupta, V., Gupta, S., Qu, L., Ihsani, A., Bizzo, B. C., et al. (2020) · Lecture Notes in Computer Science

Privacy-Preserving Technologies in DataAI in cancer detection

Cited by 202

Anatomy-specific classification of medical images using deep convolutional nets

Roth, H. R., Lee, C. T., Shin, H.-C., Seff, A., Kim, L., Yao, J., Lu, L., & Summers, R. M. (2015) · 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)

Medical Imaging and AnalysisRadiomics and Machine Learning in Medical Imaging

Cited by 188

3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training

Xia, Y., Liu, F., Yang, D., Cai, J., Yu, L., Zhu, Z., Xu, D., Yuille, A., & Roth, H. (2020) · 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)

Medical Image Segmentation TechniquesDomain Adaptation and Few-Shot Learning

Cited by 170

Hierarchical 3D fully convolutional networks for multi-organ segmentation

Roth, H. R., Oda, H., Hayashi, Y., Oda, M., Shimizu, N., Fujiwara, M., Misawa, K., & Mori, K. (2017) · arXiv

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 111

Radiomic features at CT can distinguish pancreatic cancer from noncancerous pancreas

Chen, P.-T., Chang, D., Yen, H., Liu, K.-L., Huang, S.-Y., Roth, H., Wu, M.-S., Liao, W.-C., & Wang, W. (2021) · Radiology: Imaging Cancer

Radiomics and Machine Learning in Medical ImagingPancreatic and Hepatic Oncology Research

Cited by 72

The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape

Zhao, J., Bagchi, S., Avestimehr, S., Chan, K., Chaterji, S., Dimitriadis, D., Li, J., Li, N., Nourian, A., & Roth, H. (2025) · ACM Computing Surveys

Privacy-Preserving Technologies in DataCloud Data Security Solutions

Cited by 63

Multi-task Federated Learning for Heterogeneous Pancreas Segmentation

Shen, C., Wang, P., Roth, H. R., Yang, D., Xu, D., Oda, M., Wang, W., Fuh, C.-S., Chen, P.-T., Liu, K.-L., et al. (2021) · Lecture Notes in Computer Science

Privacy-Preserving Technologies in DataMedical Image Segmentation Techniques

Cited by 36

Leveraging Mid-Level Semantic Boundary Cues for Automated Lymph Node Detection

Seff, A., Lu, L., Barbu, A., Roth, H., Shin, H.-C., & Summers, R. M. (2015) · Lecture Notes in Computer Science

AI in cancer detectionMedical Image Segmentation Techniques

Cited by 36

When Unseen Domain Generalization is Unnecessary? Rethinking Data Augmentation

Zhang, L., Wang, X., Yang, D., Sanford, T., Harmon, S., Turkbey, B., Roth, H., Myronenko, A., Xu, D., & Xu, Z. (2019) · arXiv

Domain Adaptation and Few-Shot LearningMedical Image Segmentation Techniques

Cited by 36