George Lee
Cited by
Cited by
Applications of machine learning in drug discovery and development
J Vamathevan, D Clark, P Czodrowski, I Dunham, E Ferran, G Lee, B Li, ...
Nature reviews Drug discovery 18 (6), 463-477, 2019
Image analysis and machine learning in digital pathology: Challenges and opportunities
A Madabhushi, G Lee
Medical image analysis 33, 170-175, 2016
Computer-aided prognosis: predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data
A Madabhushi, S Agner, A Basavanhally, S Doyle, G Lee
Computerized medical imaging and graphics 35 (7-8), 506-514, 2011
Investigating the efficacy of nonlinear dimensionality reduction schemes in classifying gene and protein expression studies
G Lee, C Rodriguez, A Madabhushi
IEEE/ACM Transactions on Computational Biology and Bioinformatics 5 (3), 368-384, 2008
Supervised multi-view canonical correlation analysis (sMVCCA): Integrating histologic and proteomic features for predicting recurrent prostate cancer
G Lee, A Singanamalli, H Wang, MD Feldman, SR Master, NNC Shih, ...
IEEE transactions on medical imaging 34 (1), 284-297, 2014
Co-occurring gland angularity in localized subgraphs: predicting biochemical recurrence in intermediate-risk prostate cancer patients
G Lee, R Sparks, S Ali, NNC Shih, MD Feldman, E Spangler, T Rebbeck, ...
PloS one 9 (5), e97954, 2014
Evaluating stability of histomorphometric features across scanner and staining variations: prostate cancer diagnosis from whole slide images
P Leo, G Lee, NNC Shih, R Elliott, MD Feldman, A Madabhushi
Journal of medical imaging 3 (4), 047502-047502, 2016
Nuclear shape and architecture in benign fields predict biochemical recurrence in prostate cancer patients following radical prostatectomy: preliminary findings
G Lee, RW Veltri, G Zhu, S Ali, JI Epstein, A Madabhushi
European urology focus 3 (4-5), 457-466, 2017
Cell orientation entropy (COrE): predicting biochemical recurrence from prostate cancer tissue microarrays
G Lee, S Ali, R Veltri, JI Epstein, C Christudass, A Madabhushi
Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013: 16th …, 2013
Integrated diagnostics: a conceptual framework with examples
A Madabhushi, S Doyle, G Lee, A Basavanhally, J Monaco, S Masters, ...
Clinical chemistry and laboratory medicine 48 (7), 989-998, 2010
A knowledge representation framework for integration, classification of multi-scale imaging and non-imaging data: Preliminary results in predicting prostate cancer recurrence …
G Lee, S Doyle, J Monaco, A Madabhushi, MD Feldman, SR Master, ...
2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro …, 2009
Feature importance in nonlinear embeddings (FINE): applications in digital pathology
SB Ginsburg, G Lee, S Ali, A Madabhushi
IEEE transactions on medical imaging 35 (1), 76-88, 2015
Evaluating feature selection strategies for high dimensional, small sample size datasets
A Golugula, G Lee, A Madabhushi
2011 Annual International conference of the IEEE engineering in medicine and …, 2011
Multi-modal data fusion schemes for integrated classification of imaging and non-imaging biomedical data
P Tiwari, S Viswanath, G Lee, A Madabhushi
2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro …, 2011
Advances in the computational and molecular understanding of the prostate cancer cell nucleus
NM Carleton, G Lee, A Madabhushi, RW Veltri
Journal of cellular biochemistry 119 (9), 7127-7142, 2018
An empirical comparison of dimensionality reduction methods for classifying gene and protein expression datasets
G Lee, C Rodriguez, A Madabhushi
Bioinformatics Research and Applications: Third International Symposium …, 2007
Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: concepts, workflow, and use-cases
SE Viswanath, P Tiwari, G Lee, A Madabhushi, ...
BMC medical imaging 17, 1-17, 2017
Computer extracted features from initial H&E tissue biopsies predict disease progression for prostate cancer patients on active surveillance
S Chandramouli, P Leo, G Lee, R Elliott, C Davis, G Zhu, P Fu, JI Epstein, ...
Cancers 12 (9), 2708, 2020
Association of artificial intelligence-powered and manual quantification of programmed death-ligand 1 (PD-L1) expression with outcomes in patients treated with nivolumabħipilimumab
V Baxi, G Lee, C Duan, D Pandya, DN Cohen, R Edwards, H Chang, J Li, ...
Modern Pathology 35 (11), 1529-1539, 2022
Supervised multi-view canonical correlation analysis: Fused multimodal prediction of disease diagnosis and prognosis
A Singanamalli, H Wang, G Lee, N Shih, M Rosen, S Master, ...
Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and …, 2014
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