R. Graham Barr
According to our database1,
R. Graham Barr
authored at least 23 papers
between 2008 and 2024.
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Bibliography
2024
Robust deep labeling of radiological emphysema subtypes using squeeze and excitation convolutional neural networks: The MESA Lung and SPIROMICS Studies.
CoRR, 2024
Robust Quantification of Percent Emphysema on CT via Domain Attention: the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study.
CoRR, 2024
2023
Bridging the Task Barriers: Online Knowledge Distillation Across Tasks for Semi-supervised Mediastinal Segmentation in CT.
Proceedings of the Machine Learning in Medical Imaging - 14th International Workshop, 2023
Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023
2022
J. Imaging, 2022
2021
Novel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study.
IEEE Trans. Medical Imaging, 2021
Recursive Refinement Network for Deformable Lung Registration between Exhale and Inhale CT Scans.
CoRR, 2021
Unsupervised Clustering Of Airway Tree Structures On High-Resolution CT: The Mesa Lung Study.
Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, 2021
2020
Novel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning.
CoRR, 2020
Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, 2020
2019
Unsupervised Domain Adaption With Adversarial Learning (UDAA) for Emphysema Subtyping on Cardiac CT Scans: The Mesa Study.
Proceedings of the 16th IEEE International Symposium on Biomedical Imaging, 2019
Enhanced Generative Model for Unsupervised Discovery of Spatially-Informed Macroscopic Emphysema: The Mesa Copd Study.
Proceedings of the 16th IEEE International Symposium on Biomedical Imaging, 2019
2017
Unsupervised Discovery of Spatially-Informed Lung Texture Patterns for Pulmonary Emphysema: The MESA COPD Study.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2017, 2017
Generative method to discover emphysema subtypes with unsupervised learning using lung macroscopic patterns (LMPS): The MESA COPD study.
Proceedings of the 14th IEEE International Symposium on Biomedical Imaging, 2017
2016
Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study.
Proceedings of the Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging, 2016
Emphysema Quantification on Cardiac CT Scans Using Hidden Markov Measure Field Model: The MESA Lung Study.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016, 2016
2015
Equating emphysema scores and segmentations across CT reconstructions: A comparison study.
Proceedings of the 12th IEEE International Symposium on Biomedical Imaging, 2015
Sparse sampling and unsupervised learning of lung texture patterns in pulmonary emphysema: MESA COPD study.
Proceedings of the 12th IEEE International Symposium on Biomedical Imaging, 2015
2014
Adaptive Quantification and Longitudinal Analysis of Pulmonary Emphysema With a Hidden Markov Measure Field Model.
IEEE Trans. Medical Imaging, 2014
2013
Robust quantification of pulmonary emphysema with a Hidden Markov Measure Field model.
Proceedings of the 10th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2013
2010
Variation compensation and analysis on diaphragm curvature analysis for emphysema quantification on whole lung CT scans.
Proceedings of the Medical Imaging 2010: Computer-Aided Diagnosis, 2010
2009
Emphysema quantification from CT scans using novel application of diaphragm curvature estimation: comparison with standard quantification methods and pulmonary function data.
Proceedings of the Medical Imaging 2009: Computer-Aided Diagnosis, 2009
2008
Proceedings of the Medical Imaging 2008: Computer-Aided Diagnosis, 2008