George Lee

Orcid: 0000-0002-8705-9210

Affiliations:
  • Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH, USA
  • State University of New Jersey, Department of Biomedical Engineering, NJ, USA
  • Rutgers University, Department of Biomedical Engineering, Piscataway, NJ, USA


According to our database1, George Lee authored at least 18 papers between 2007 and 2017.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2017
Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: concepts, workflow, and use-cases.
BMC Medical Imaging, 2017

2016
Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology.
IEEE Trans. Medical Imaging, 2016

Image analysis and machine learning in digital pathology: Challenges and opportunities.
Medical Image Anal., 2016

Evaluating stability of histomorphometric features across scanner and staining variations: predicting biochemical recurrence from prostate cancer whole slide images.
Proceedings of the Medical Imaging 2016: Digital Pathology, San Diego, California, United States, 27 February, 2016

2015
Supervised Multi-View Canonical Correlation Analysis (sMVCCA): Integrating Histologic and Proteomic Features for Predicting Recurrent Prostate Cancer.
IEEE Trans. Medical Imaging, 2015

2013
Cell Orientation Entropy (COrE): Predicting Biochemical Recurrence from Prostate Cancer Tissue Microarrays.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2013, 2013

Variable Importance in Nonlinear Kernels (VINK): Classification of Digitized Histopathology.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2013, 2013

Co-occurring gland tensors in localized cluster graphs: Quantitative histomorphometry for predicting biochemical recurrence for intermediate grade prostate cancer.
Proceedings of the 10th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2013

2011
Computer-aided prognosis: Predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data.
Comput. Medical Imaging Graph., 2011

Supervised Regularized Canonical Correlation Analysis: integrating histologic and proteomic measurements for predicting biochemical recurrence following prostate surgery.
BMC Bioinform., 2011

Multi-modal data fusion schemes for integrated classification of imaging and non-imaging biomedical data.
Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2011

Supervised regularized canonical correlation analysis: Integrating histologic and proteomic data for predicting biochemical failures.
Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011

Evaluating feature selection strategies for high dimensional, small sample size datasets.
Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011

2010
Semi-Supervised Graph Embedding Scheme with Active Learning (SSGEAL): Classifying High Dimensional Biomedical Data.
Proceedings of the Pattern Recognition in Bioinformatics, 2010

Computer-aided prognosis: predicting patient and disease outcome via multi-modal image analysis.
Proceedings of the 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2010

2009
A Knowledge Representation Framework for Integration, Classification of Multi-Scale Imaging and Non-Imaging Data: Preliminary Results in Predicting Prostate Cancer Recurrence by Fusing Mass Spectrometry and Histology.
Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA, June 28, 2009

2008
Investigating the Efficacy of Nonlinear Dimensionality Reduction Schemes in Classifying Gene and Protein Expression Studies.
IEEE ACM Trans. Comput. Biol. Bioinform., 2008

2007
An Empirical Comparison of Dimensionality Reduction Methods for Classifying Gene and Protein Expression Datasets.
Proceedings of the Bioinformatics Research and Applications, Third International Symposium, 2007


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