Computer-aided prognosis: Predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data
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George Lee | Anant Madabhushi | Ajay Basavanhally | Scott Doyle | Shannon Agner | A. Madabhushi | S. Agner | Scott Doyle | Ajay Basavanhally | George Lee | A. Basavanhally
[1] Marinette Revenu,et al. Neighborhood graphs and image processing , 1996, Other Conferences.
[2] Anant Madabhushi,et al. Integrating structural and functional imaging for computer assisted detection of prostate cancer on multi-protocol in vivo 3 Tesla MRI , 2009, Medical Imaging.
[3] Arend Heerschap,et al. Combination of feature‐reduced MR spectroscopic and MR imaging data for improved brain tumor classification , 2005, NMR in biomedicine.
[4] Paul L. Rosin,et al. Selection of the optimal parameter value for the Isomap algorithm , 2006, Pattern Recognit. Lett..
[5] Dimitris N. Metaxas,et al. Automated detection of prostatic adenocarcinoma from high-resolution ex vivo MRI , 2005, IEEE Transactions on Medical Imaging.
[6] S. Provencher. Estimation of metabolite concentrations from localized in vivo proton NMR spectra , 1993, Magnetic resonance in medicine.
[7] A. Madabhushi,et al. Integrated diagnostics: a conceptual framework with examples , 2010, Clinical chemistry and laboratory medicine.
[8] Volker Tresp,et al. Heterogenous Data Fusion via a Probabilistic Latent-Variable Model , 2004, ARCS.
[9] Vural Ozdemir,et al. Mapping translational research in personalized therapeutics: from molecular markers to health policy. , 2007, Pharmacogenomics.
[10] Arend Heerschap,et al. A chemometric approach for brain tumor classification using magnetic resonance imaging and spectroscopy. , 2003, Analytical chemistry.
[11] Jun Kong,et al. Feature-based registration of histopathology images with different stains: An application for computerized follicular lymphoma prognosis , 2009, Comput. Methods Programs Biomed..
[12] D. Grignon,et al. Diagnostic and prognostic markers for human prostate cancer , 1997, The Prostate.
[13] Harald Treuer,et al. COMBINED X‐RAY AND MAGNETIC RESONANCE IMAGING FACILITY: APPLICATION TO IMAGE‐GUIDED STEREOTACTIC AND FUNCTIONAL NEUROSURGERY , 2007, Neurosurgery.
[14] Gabriela Alexe,et al. Towards Improved Cancer Diagnosis and Prognosis Using Analysis of Gene Expression Data and Computer Aided Imaging , 2009, Experimental biology and medicine.
[15] Eric Bruno,et al. Design of Multimodal Dissimilarity Spaces for Retrieval of Video Documents , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[16] Lawrence J. Mazlack,et al. Multi-modal Data Fusion: A Description , 2004, KES.
[17] Atif R Mirza,et al. An architectural selection framework for data fusion in sensor platforms , 2006 .
[18] Heng Tao Shen,et al. Principal Component Analysis , 2009, Encyclopedia of Biometrics.
[19] E. Mittendorf,et al. High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates , 2008 .
[20] A. Stephenson,et al. The value of radiotherapy in treating recurrent prostate cancer after radical prostatectomy , 2004, Nature Clinical Practice Urology.
[21] D. Bostwick,et al. Staging of early prostate cancer: a proposed tumor volume-based prognostic index. , 1993, Urology.
[22] Jianbo Shi,et al. Graph Embedding to Improve Supervised Classification and Novel Class Detection: Application to Prostate Cancer , 2005, MICCAI.
[23] Gary Benson,et al. Evaluating distance functions for clustering tandem repeats. , 2005, Genome informatics. International Conference on Genome Informatics.
[24] William Stafford Noble,et al. Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure , 2006, Bioinform..
[25] M. Kattan,et al. Preoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy. , 2006, Journal of the National Cancer Institute.
[26] A. Shanberg,et al. Ureteric and urethral stenosis: A complication of BK virus infection in a pediatric renal transplant patient , 2007, Pediatric transplantation.
[27] Joseph C. Aman,et al. An Evaluation of Information Content as a Metric for the Inference of Putative Conserved Noncoding Regions in DNA Sequences Using a Genetic Algorithms Approach , 2008, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[28] Jing Li,et al. Heterogeneous data fusion for alzheimer's disease study , 2008, KDD.
[29] Lijun Jiang,et al. Using locally estimated geodesic distance to optimize neighborhood graph for isometric data embedding , 2008, Pattern Recognit..
[30] J. Sudbø,et al. New Algorithms Based on the Voronoi Diagram Applied in a Pilot Study on Normal Mucosa and Carcinomas , 2000, Analytical cellular pathology : the journal of the European Society for Analytical Cellular Pathology.
[31] Raphael Marcelpoil,et al. Prognostic Value of Graph Theory-Based Tissue Architecture Analysis in Carcinomas of the Tongue , 2000, Laboratory Investigation.
[32] C. Floyd,et al. Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis. , 2006, Medical physics.
[33] George Lee,et al. Computer-aided prognosis: Predicting patient and disease outcome via multi-modal image analysis , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[34] L. Klotz. Active surveillance for prostate cancer: for whom? , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[35] Anant Madabhushi,et al. A Boosted Bayesian Multiresolution Classifier for Prostate Cancer Detection From Digitized Needle Biopsies , 2012, IEEE Transactions on Biomedical Engineering.
[36] Anant Madabhushi,et al. Textural Kinetics: A Novel Dynamic Contrast-Enhanced (DCE)-MRI Feature for Breast Lesion Classification , 2011, Journal of Digital Imaging.
[37] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.
[38] George Lee,et al. An Empirical Comparison of Dimensionality Reduction Methods for Classifying Gene and Protein Expression Datasets , 2007, ISBRA.
[39] R. Marcelpoil,et al. Normalization of the minimum spanning tree. , 1993, Analytical cellular pathology : the journal of the European Society for Analytical Cellular Pathology.
[40] D. Bostwick. Grading prostate cancer. , 1994, American journal of clinical pathology.
[41] Michael W Kattan,et al. Defining biochemical recurrence of prostate cancer after radical prostatectomy: a proposal for a standardized definition. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[42] Anant Madabhushi,et al. Computer-aided prognosis of ER+ breast cancer histopathology and correlating survival outcome with Oncotype DX assay , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[43] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[44] A. Madabhushi,et al. Investigating the Efficacy of Nonlinear Dimensionality Reduction Schemes in Classifying Gene and Protein Expression Studies , 2008, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[45] Lei Guo,et al. Brain tissue segmentation based on DTI data , 2007, NeuroImage.
[46] B. Nicolas Bloch,et al. An illustration of the potential for mapping MRI/MRS parameters with genetic over-expression profiles in human prostate cancer , 2008, Magnetic Resonance Materials in Physics, Biology and Medicine.
[47] Michael W Kattan,et al. Postoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[48] Anant Madabhushi,et al. A meta-classifier for detecting prostate cancer by quantitative integration of in vivo magnetic resonance spectroscopy and magnetic resonance imaging , 2008, SPIE Medical Imaging.
[49] Andreas Rauber,et al. Decision Manifolds—A Supervised Learning Algorithm Based on Self-Organization , 2008, IEEE Transactions on Neural Networks.
[50] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[51] G. Bhanot,et al. Identification of a microRNA panel for clear-cell kidney cancer. , 2010, Urology.
[52] Christopher J. C. Burges,et al. A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.
[53] Daniel Rueckert,et al. Multiclassifier Fusion in Human Brain MR Segmentation: Modelling Convergence , 2006, MICCAI.
[54] Gabriela Alexe,et al. High expression of lymphocyte-associated genes in node-negative HER2+ breast cancers correlates with lower recurrence rates. , 2007, Cancer research.
[55] Magnus Hellström,et al. Proteomic analysis of protein expression in prostate cancer. , 2005, Analytical and quantitative cytology and histology.
[56] Anant Madabhushi,et al. A Hierarchical Unsupervised Spectral Clustering Scheme for Detection of Prostate Cancer from Magnetic Resonance Spectroscopy (MRS) , 2007, MICCAI.
[57] Nello Cristianini,et al. Kernel-Based Data Fusion and Its Application to Protein Function Prediction in Yeast , 2003, Pacific Symposium on Biocomputing.
[58] A W Smeulders,et al. An analysis of pathology knowledge and decision making for the development of artificial intelligence-based consulting systems. , 1989, Analytical and quantitative cytology and histology.
[59] A. Khatami,et al. Early Prostate Cancer, On prognostic markers and predictors of treatment outcome after radical prostatectomy. , 2007 .
[60] David G. Stork,et al. Pattern Classification , 1973 .
[61] Karl J. Friston,et al. Functional topography: multidimensional scaling and functional connectivity in the brain. , 1996, Cerebral cortex.
[62] C G Roehrborn,et al. Gleason scores from prostate biopsies obtained with 18-gauge biopsy needles poorly predict Gleason scores of radical prostatectomy specimens. , 1998, European urology.
[63] Hans Lilja,et al. Results of a randomized, population‐based study of biennial screening using serum prostate‐specific antigen measurement to detect prostate carcinoma , 2004, Cancer.
[64] A. Madabhushi. Digital pathology image analysis: opportunities and challenges. , 2009, Imaging in medicine.
[65] A. W. Simonetti,et al. The use of multivariate MR imaging intensities versus metabolic data from MR spectroscopic imaging for brain tumour classification. , 2005, Journal of magnetic resonance.
[66] Giuseppe Lippi,et al. Wisdom of theragnostics, other changes. , 2008, MLO: medical laboratory observer.
[67] Torsten Rohlfing,et al. Information Fusion in Biomedical Image Analysis: Combination of Data vs. Combination of Interpretations , 2005, IPMI.
[68] T. Turkington,et al. PET and brain tumor image fusion. , 2004, Cancer journal.
[69] D. Gleason,et al. Histologic grading of prostate cancer: a perspective. , 1992, Human pathology.
[70] J. Sudbø,et al. Caveats: Numerical Requirements in Graph Theory Based Quantitation of Tissue Architecture , 2000, Analytical cellular pathology : the journal of the European Society for Analytical Cellular Pathology.
[71] Anant Madabhushi,et al. A consensus embedding approach for segmentation of high resolution in vivo prostate magnetic resonance imagery , 2008, SPIE Medical Imaging.
[72] Richard O. Duda,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.
[73] Alain Cariou,et al. Toward theragnostics , 2009, Critical care medicine.
[74] Axel Saalbach,et al. Image fusion for dynamic contrast enhanced magnetic resonance imaging , 2004, Biomedical engineering online.
[75] D. Bostwick,et al. Interobserver reproducibility of Gleason grading of prostatic carcinoma: urologic pathologists. , 2001, Human pathology.
[76] Purang Abolmaesumi,et al. High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models , 2010, Medical Image Anal..
[77] Isabelle Bloch,et al. Representation and fusion of heterogeneous fuzzy information in the 3D space for model-based structural recognition--Application to 3D brain imaging , 2003, Artif. Intell..
[78] Anant Madabhushi,et al. Spectral Embedding Based Probabilistic Boosting Tree (ScEPTre): Classifying High Dimensional Heterogeneous Biomedical Data , 2009, MICCAI.
[79] Anant Madabhushi,et al. AUTOMATED GRADING OF PROSTATE CANCER USING ARCHITECTURAL AND TEXTURAL IMAGE FEATURES , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[80] C. Mascott,et al. Image fusion of fluid-attenuated inversion recovery magnetic resonance imaging sequences for surgical image guidance. , 2007, Surgical neurology.
[81] G. Bhanot,et al. Manifold learning with graph-based features for identifying extent of lymphocytic infiltration from high grade , HER 2 + breast cancer histology , 2008 .
[82] Jia Wei,et al. Adaptive neighborhood selection for manifold learning , 2008, 2008 International Conference on Machine Learning and Cybernetics.
[83] Robert E Lenkinski,et al. Prostate cancer: accurate determination of extracapsular extension with high-spatial-resolution dynamic contrast-enhanced and T2-weighted MR imaging--initial results. , 2007, Radiology.
[84] John K. Tsotsos,et al. Parameterless Isomap with Adaptive Neighborhood Selection , 2006, DAGM-Symposium.
[85] Anant Madabhushi,et al. Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[86] Pierre I Karakiewicz,et al. Can predictive models for prostate cancer patients derived in the United States of America be utilized in European patients? A validation study of the Partin tables. , 2003, European urology.
[87] Andrew Janowczyk,et al. Hierarchical Normalized Cuts: Unsupervised Segmentation of Vascular Biomarkers from Ovarian Cancer Tissue Microarrays , 2009, MICCAI.
[88] Rainer Schrader,et al. Fast and robust registration of PET and MR images of human brain , 2004, NeuroImage.
[89] R Albert,et al. Three-dimensional image processing for morphometric analysis of epithelium sections. , 1992, Cytometry.
[90] George Lee,et al. 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 , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[91] Abdul Ghapor Hussin,et al. Computerized Medical Imaging and Graphics. , 2011 .
[92] Amod Jog,et al. Classifying Ayurvedic Pulse Signals Via Consensus Locally Linear Embedding , 2009, BIOSIGNALS.
[93] Kazuyuki Aihara,et al. Sequential Data Fusion via Vector Spaces: Fusion of Heterogeneous Data in the Complex Domain , 2007, J. VLSI Signal Process..
[94] Hongyuan Zha,et al. Adaptive Manifold Learning , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[95] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[96] Jun Nakashima,et al. Prognostic Implication of Microvascular Invasion in Biochemical Failure in Patients Treated with Radical Prostatectomy , 2003, Urologia Internationalis.
[97] George Coukos,et al. Intratumoral T cells, recurrence, and survival in epithelial ovarian cancer. , 2003, The New England journal of medicine.
[98] David L. Hall,et al. Perspectives on the fusion of image and non-image data , 2003, 32nd Applied Imagery Pattern Recognition Workshop, 2003. Proceedings..
[99] Anant Madabhushi,et al. Consensus-Locally Linear Embedding (C-LLE): Application to Prostate Cancer Detection on Magnetic Resonance Spectroscopy , 2008, MICCAI.
[100] S. Dube,et al. Content Based Image Retrieval for MR Image Studies of Brain Tumors , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.
[101] Gyan Bhanot,et al. Computerized Image-Based Detection and Grading of Lymphocytic Infiltration in HER2+ Breast Cancer Histopathology , 2010, IEEE Transactions on Biomedical Engineering.
[102] Graeme P. Penney,et al. Estimating and resolving uncertainty in cardiac respiratory motion modelling , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).
[103] Shigeo Abe DrEng. Pattern Classification , 2001, Springer London.