Rubin, Daniel L.

Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease

Supekar, K., Menon, V., Rubin, D., Musen, M., & Greicius, M. D. (2008) · PLoS Computational Biology

Functional Brain Connectivity StudiesDementia and Cognitive Impairment Research

Cited by 1,148

Deep learning for brain MRI segmentation: state of the art and future directions

Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L., & Erickson, B. J. (2017) · Journal of Digital Imaging

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 1,129

Preparing Medical Imaging Data for Machine Learning

Willemink, M. J., Koszek, W. A., Hardell, C., Wu, J., Fleischmann, D., Harvey, H., Folio, L. R., Summers, R. M., Rubin, D. L., & Lungren, M. P. (2020) · Radiology

Artificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

Cited by 1,020

Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

Yu, K.-H., Zhang, C., Berry, G. J., Altman, R. B., Ré, C., Rubin, D. L., & Snyder, M. (2016) · Nature Communications

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 972

A curated mammography data set for use in computer-aided detection and diagnosis research

Lee, R. S., Gimenez, F., Hoogi, A., Miyake, K. K., Gorovoy, M., & Rubin, D. L. (2017) · Scientific Data

AI in cancer detectionDigital Radiography and Breast Imaging

Cited by 859

Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms

Schaffter, T., Buist, D. S. M., Lee, C. I., Nikulin, Y., Ribli, D., Guan, Y., Lotter, W., Jie, Z., Du, H., Wang, S., et al. (2020) · JAMA Network Open

AI in cancer detectionDigital Radiography and Breast Imaging

Cited by 409

Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study

Yamashita, R., Long, J., Longacre, T., Peng, L., Berry, G., Martin, B., Higgins, J., Rubin, D. L., & Shen, J. (2021) · The Lancet Oncology

AI in cancer detectionGenetic factors in colorectal cancer

Cited by 391

Content-based image retrieval in radiology: current status and future directions

Akgül, C. B., Rubin, D. L., Napel, S., Beaulieu, C. F., Greenspan, H., & Acar, B. (2011) · Journal of Digital Imaging

Image Retrieval and Classification TechniquesRadiomics and Machine Learning in Medical Imaging

Cited by 388

Distributed deep learning networks among institutions for medical imaging

Chang, K., Balachandar, N., Lam, C., Yi, D., Brown, J., Beers, A., Rosen, B., Rubin, D. L., & Kalpathy-Cramer, J. (2018) · Journal of the American Medical Informatics Association

Privacy-Preserving Technologies in DataArtificial Intelligence in Healthcare and Education

Cited by 340

A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound

Lekadir, K., Galimzianova, A., Betriu, A., del Mar Vila, M., Igual, L., Rubin, D. L., Fernandez, E., Radeva, P., & Napel, S. (2017) · IEEE Journal of Biomedical and Health Informatics

Cerebrovascular and Carotid Artery DiseasesMedical Image Segmentation Techniques

Cited by 212

Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs

Dunnmon, J. A., Yi, D., Langlotz, C. P., Ré, C., Rubin, D. L., & Lungren, M. P. (2019) · Radiology

COVID-19 diagnosis using AIArtificial Intelligence in Healthcare and Education

Cited by 206

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

Regulatory Frameworks for Development and Evaluation of Artificial Intelligence–Based Diagnostic Imaging Algorithms: Summary and Recommendations

Larson, D. B., Harvey, H., Rubin, D. L., Irani, N., Tse, J. R., & Langlotz, C. P. (2021) · Journal of the American College of Radiology

Artificial Intelligence in Healthcare and EducationHealthcare Regulation

Cited by 198

Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging

Yan, R., Qu, L., Wei, Q., Huang, S.-C., Shen, L., Rubin, D. L., Xing, L., & Zhou, Y. (2023) · IEEE Transactions on Medical Imaging

Privacy-Preserving Technologies in DataDomain Adaptation and Few-Shot Learning

Cited by 183

Predictive radiogenomics modeling of EGFR mutation status in lung cancer

Gevaert, O., Echegaray, S., Khuong, A., Hoang, C. D., Shrager, J. B., Jensen, K. C., Berry, G. J., Guo, H. H., Lau, C., Plevritis, S. K., et al. (2017) · Scientific Reports

Radiomics and Machine Learning in Medical ImagingLung Cancer Diagnosis and Treatment

Cited by 160

Putting the data before the algorithm in big data addressing personalized healthcare

Cahan, E. M., Hernandez-Boussard, T., Thadaney-Israni, S., & Rubin, D. L. (2019) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 149

Artificial Intelligence in Imaging: The Radiologist’s Role

Rubin, D. L. (2019) · Journal of the American College of Radiology

Artificial Intelligence in Healthcare and EducationRadiology practices and education

Cited by 110

Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence

Bai, X., Wang, H., Ma, L., Xu, Y., Gan, J., Fan, Z., Yang, F., Ma, K., Yang, J., Bai, S., et al. (2021) · Nature Machine Intelligence

COVID-19 diagnosis using AIPrivacy-Preserving Technologies in Data

Cited by 75

Magnetic resonance imaging and molecular features associated with tumor-infiltrating lymphocytes in breast cancer

Wu, J., Li, X., Teng, X., Rubin, D. L., Napel, S., Daniel, B. L., & Li, R. (2018) · Breast Cancer Research

MRI in cancer diagnosisRadiomics and Machine Learning in Medical Imaging

Cited by 53

Handling missing MRI sequences in deep learning segmentation of brain metastases: a multicenter study

Grøvik, E., Yi, D., Iv, M., Tong, E., Nilsen, L. B., Latysheva, A., Saxhaug, C., Jacobsen, K. D., Helland, Å., Emblem, K. E., et al. (2021) · npj Digital Medicine

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 51

2.5D and 3D segmentation of brain metastases with deep learning on multinational MRI data

Ottesen, J. A., Yi, D., Tong, E., Iv, M., Latysheva, A., Saxhaug, C., Jacobsen, K. D., Helland, Å., Emblem, K. E., Rubin, D. L., et al. (2023) · Frontiers in Neuroinformatics

Brain Tumor Detection and ClassificationMedical Image Segmentation Techniques

Cited by 32

RaLEs: a Benchmark for Radiology Language Evaluations

Chaves, J. Z., Bhaskhar, N., Attias, M., Delbrouck, J.-B., Rubin, D., Loening, A., Langlotz, C., & Chaudhari, A. (2023) · Advances in Neural Information Processing Systems 36

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 0