Stumpe, Martin C.

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

PLANETARY CANDIDATES OBSERVED BY KEPLER . III. ANALYSIS OF THE FIRST 16 MONTHS OF DATA

Batalha, N. M., Rowe, J. F., Bryson, S. T., Barclay, T., Burke, C. J., Caldwell, D. A., Christiansen, J. L., Mullally, F., Thompson, S. E., Brown, T. M., et al. (2013) · The Astrophysical Journal Supplement Series

Stellar, planetary, and galactic studiesAstronomy and Astrophysical Research

Cited by 1,042

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

Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making

Cai, C. J., Reif, E., Hegde, N., Hipp, J., Kim, B., Smilkov, D., Wattenberg, M., Viegas, F., Corrado, G. S., Stumpe, M. C., et al. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

Artificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)

Cited by 405

An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis

Chen, P.-H. C., Gadepalli, K., MacDonald, R., Liu, Y., Kadowaki, S., Nagpal, K., Kohlberger, T., Dean, J., Corrado, G. S., Hipp, J. D., et al. (2019) · Nature Medicine

AI in cancer detectionAugmented Reality Applications

Cited by 342

Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network

Raghunath, S., Ulloa Cerna, A. E., Jing, L., vanMaanen, D. P., Stough, J., Hartzel, D. N., Leader, J. B., Kirchner, H. L., Stumpe, M. C., Hafez, A., et al. (2020) · Nature Medicine

ECG Monitoring and AnalysisMachine Learning in Healthcare

Cited by 314

Deep learning-based survival prediction for multiple cancer types using histopathology images

Wulczyn, E., Steiner, D. F., Xu, Z., Sadhwani, A., Wang, H., Flament-Auvigne, I., Mermel, C. H., Chen, P.-H. C., Liu, Y., & Stumpe, M. C. (2020) · PLOS ONE

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 266

Deep Orthogonal Fusion: Multimodal Prognostic Biomarker Discovery Integrating Radiology, Pathology, Genomic, and Clinical Data

Braman, N., Gordon, J. W. H., Goossens, E. T., Willis, C., Stumpe, M. C., & Venkataraman, J. (2021) · Lecture Notes in Computer Science

Radiomics and Machine Learning in Medical ImagingGlioma Diagnosis and Treatment

Cited by 107

A pan-cancer organoid platform for precision medicine

Larsen, B. M., Kannan, M., Langer, L. F., Leibowitz, B. D., Bentaieb, A., Cancino, A., Dolgalev, I., Drummond, B. E., Dry, J. R., Ho, C.-S., et al. (2021) · Cell Reports

Cancer Cells and Metastasis3D Printing in Biomedical Research

Cited by 105

Planetary Candidates Observed by Kepler, III: Analysis of the First 16 Months of Data

Batalha, N. M., Rowe, J. F., Bryson, S. T., Barclay, T., Burke, C. J., Caldwell, D. A., Christiansen, J. L., Mullally, F., Thompson, S. E., Brown, T. M., et al. (2012) · arXiv

Stellar, planetary, and galactic studiesAstronomy and Astrophysical Research

Cited by 10

A Self-Supervised Framework for Laboratory Data Imputation in Electronic Health Records

Heilbroner, S. P., Carter, C., Vidmar, D. M., Mueller, E. T., Stumpe, M. C., & Miotto, R. (2025) · Communications Medicine

Machine Learning in HealthcareElectronic Health Records Systems

Cited by 1