Peng, Lily

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 7,697

International evaluation of an AI system for breast cancer screening

McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., et al. (2020) · Nature

AI in cancer detectionDigital Radiography and Breast Imaging

Cited by 3,287

End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G., et al. (2019) · Nature Medicine

AI in cancer detectionLung Cancer Diagnosis and Treatment

Cited by 2,167

Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning

Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., Peng, L., & Webster, D. R. (2018) · Nature Biomedical Engineering

Retinal Imaging and AnalysisArtificial Intelligence in Healthcare

Cited by 1,865

Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence

Collins, G. S., Dhiman, P., Andaur Navarro, C. L., Ma, J., Hooft, L., Reitsma, J. B., Logullo, P., Beam, A. L., Peng, L., Van Calster, B., et al. (2021) · BMJ Open

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 852

Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy

Krause, J., Gulshan, V., Rahimy, E., Karth, P., Widner, K., Corrado, G. S., Peng, L., & Webster, D. R. (2018) · Ophthalmology

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 564

Detecting Cancer Metastases on Gigapixel Pathology Images

Liu, Y., Gadepalli, K., Norouzi, M., Dahl, G. E., Kohlberger, T., Boyko, A., Venugopalan, S., Timofeev, A., Nelson, P. Q., Corrado, G. S., et al. (2017) · arXiv

AI in cancer detectionCell Image Analysis Techniques

Cited by 506

Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer

Nagpal, K., Foote, D., Liu, Y., Chen, P.-H. C., Wulczyn, E., Tan, F., Olson, N., Smith, J. L., Mohtashamian, A., Wren, J. H., et al. (2019) · npj Digital Medicine

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 495

Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer

Steiner, D. F., MacDonald, R., Liu, Y., Truszkowski, P., Hipp, J. D., Gammage, C., Thng, F., Peng, L., & Stumpe, M. C. (2018) · American Journal of Surgical Pathology

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 489

Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy

Sayres, R., Taly, A., Rahimy, E., Blumer, K., Coz, D., Hammel, N., Krause, J., Narayanaswamy, A., Rastegar, Z., Wu, D., et al. (2019) · Ophthalmology

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 418

Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India

Gulshan, V., Rajan, R. P., Widner, K., Wu, D., Wubbels, P., Rhodes, T., Whitehouse, K., Coram, M., Corrado, G., Ramasamy, K., et al. (2019) · JAMA Ophthalmology

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 303

How to develop machine learning models for healthcare

Chen, P.-H. C., Liu, Y., & Peng, L. (2019) · Nature Materials

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 274

Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging

Azizi, S., Culp, L., Freyberg, J., Mustafa, B., Baur, S., Kornblith, S., Chen, T., Tomasev, N., Mitrović, J., Strachan, P., et al. (2023) · Nature Biomedical Engineering

Domain Adaptation and Few-Shot LearningArtificial Intelligence in Healthcare and Education

Cited by 250

Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program

Ruamviboonsuk, P., Krause, J., Chotcomwongse, P., Sayres, R., Raman, R., Widner, K., Campana, B. J. L., Phene, S., Hemarat, K., Tadarati, M., et al. (2019) · npj Digital Medicine

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 233

Detection of anaemia from retinal fundus images via deep learning

Mitani, A., Huang, A., Venugopalan, S., Corrado, G. S., Peng, L., Webster, D. R., Hammel, N., Liu, Y., & Varadarajan, A. V. (2019) · Nature Biomedical Engineering

Retinal Imaging and AnalysisArtificial Intelligence in Healthcare

Cited by 230

Deep Learning and Glaucoma Specialists

Phene, S., Dunn, R. C., Hammel, N., Liu, Y., Krause, J., Kitade, N., Schaekermann, M., Sayres, R., Wu, D. J., Bora, A., et al. (2019) · Ophthalmology

Retinal Imaging and AnalysisGlaucoma and retinal disorders

Cited by 186

Development and assessment of an artificial intelligence–based tool for skin condition diagnosis by primary care physicians and nurse practitioners in teledermatology practices

Jain, A., Way, D., Gupta, V., Gao, Y., de Oliveira Marinho, G., Hartford, J., Sayres, R., Kanada, K., Eng, C., Nagpal, K., et al. (2021) · JAMA Network Open

Artificial Intelligence in Healthcare and EducationCutaneous Melanoma Detection and Management

Cited by 164

Determining breast cancer biomarker status and associated morphological features using deep learning

Gamble, P., Jaroensri, R., Wang, H., Tan, F., Moran, M., Brown, T., Flament-Auvigne, I., Rakha, E. A., Toss, M., Dabbs, D. J., et al. (2021) · Communications Medicine

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 154

Pathologist validation of a machine learning–derived feature for colon cancer risk stratification

L’Imperio, V., Wulczyn, E., Plass, M., Müller, H., Tamini, N., Gianotti, L., Zucchini, N., Reihs, R., Corrado, G. S., Webster, D. R., et al. (2023) · JAMA Network Open

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 57

A mobile-optimized artificial intelligence system for gestational age and fetal malpresentation assessment

Gomes, R. G., Vwalika, B., Lee, C., Willis, A., Sieniek, M., Price, J. T., Chen, C., Kasaro, M. P., Taylor, J. A., Stringer, E. M., et al. (2022) · Communications Medicine

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 47

Remote Tool-Based Adjudication for Grading Diabetic Retinopathy

Schaekermann, M., Hammel, N., Terry, M., Ali, T. K., Liu, Y., Basham, B., Campana, B., Chen, W., Ji, X., Krause, J., et al. (2019) · Translational Vision Science & Technology

Retinal Imaging and AnalysisRetinal Diseases and Treatments

Cited by 19