Guttag, John

An Unsupervised Learning Model for Deformable Medical Image Registration

Balakrishnan, G., Zhao, A., Sabuncu, M. R., Dalca, A. V., & Guttag, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Medical Image Segmentation TechniquesDomain Adaptation and Few-Shot Learning

Cited by 599

What is the State of Neural Network Pruning?

Blalock, D., Ortiz, J. J. G., Frankle, J., & Guttag, J. (2020) · arXiv

Advanced Neural Network ApplicationsMachine Learning and Data Classification

Cited by 560

Do as AI say: susceptibility in deployment of clinical decision-aids

Gaube, S., Suresh, H., Raue, M., Merritt, A., Berkowitz, S. J., Lermer, E., Coughlin, J. F., Guttag, J. V., Colak, E., & Ghassemi, M. (2021) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationClinical Reasoning and Diagnostic Skills

Cited by 543

Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces

Dalca, A. V., Balakrishnan, G., Guttag, J., & Sabuncu, M. R. (2019) · Medical Image Analysis

Medical Image Segmentation Techniques3D Shape Modeling and Analysis

Cited by 467

UniverSeg: Universal Medical Image Segmentation

Butoi, V. I., Gonzalez Ortiz, J. J., Ma, T., Sabuncu, M. R., Guttag, J., & Dalca, A. V. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Medical Image Segmentation TechniquesDomain Adaptation and Few-Shot Learning

Cited by 195

Incorporating temporal EHR data in predictive models for risk stratification of renal function deterioration

Singh, A., Nadkarni, G., Gottesman, O., Ellis, S. B., Bottinger, E. P., & Guttag, J. V. (2015) · Journal of Biomedical Informatics

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 136

A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictions

Wiens, J., Guttag, J., & Horvitz, E. (2014) · Journal of the American Medical Informatics Association

Clostridium difficile and Clostridium perfringens researchMachine Learning in Healthcare

Cited by 123

A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle

Suresh, H., & Guttag, J. (2021) · Equity and Access in Algorithms, Mechanisms, and Optimization

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 82

A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle

Suresh, H., & Guttag, J. V. (2019) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 82

Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU

Suresh, H., Gong, J. J., & Guttag, J. V. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 54

Predicting Clinical Outcomes Across Changing Electronic Health Record Systems

Gong, J. J., Naumann, T., Szolovits, P., & Guttag, J. V. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Machine Learning in HealthcareElectronic Health Records Systems

Cited by 34

EXTRACT: Strong Examples from Weakly-Labeled Sensor Data

Blalock, D. W., & Guttag, J. V. (2016) · 2016 IEEE 16th International Conference on Data Mining (ICDM)

Time Series Analysis and ForecastingContext-Aware Activity Recognition Systems

Cited by 7

Walk the talk? Measuring the faithfulness of large language model explanations

Matton, K., Ness, R. O., Guttag, J., & Kıcıman, E. (2025) · arXiv

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 1