A quantitative data representation framework for structural and functional MR Imaging with application to prostate cancer detection

OF THE DISSERTATION A Quantitative Data Representation Framework for Structural and Functional MR Imaging with Application to Prostate Cancer Detection by Satish Easwar Viswanath Dissertation Director: Anant Madabhushi Prostate cancer (CaP) is currently the second leading cause of cancer-related deaths in the United States among men, but there is a paucity of non-invasive image-based information for CaP detection and staging in vivo. Studies have shown the utility of multi-protocol magnetic resonance imaging (MRI) to improve CaP detection accuracy by using T2-weighted (T2w), dynamic contrast enhanced (DCE), and diffusion weighted (DWI) MRI. In this thesis, we present methods for quantitative representation of structural (T2w) and functional (DCE, DWI) imaging data with the objective of building automated classifiers to improve CaP detection accuracy in vivo. In vivo disease presence was quantified via extraction of textural signatures from T2w MRI. Evaluation of these signatures showed that CaP appearance within each of the two dominant prostate regions (central gland, peripheral zone) is significantly different. A classifier trained on zone-specific features also yielded a higher detection accuracy compared to a simpler, monolithic combination of all the texture features. While a number of automated classifiers are available, classifier choice must account for limitations in dataset size and annotation (such as with in vivo prostate MRI). A comprehensive evaluation of different classifier schemes was undertaken for the specific

[1]  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.

[2]  Paul L. Rosin,et al.  Selection of the optimal parameter value for the Isomap algorithm , 2006, Pattern Recognit. Lett..

[3]  Maher Moakher,et al.  Symmetric Positive-Definite Matrices: From Geometry to Applications and Visualization , 2006, Visualization and Processing of Tensor Fields.

[4]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[5]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[6]  David M. Rocke,et al.  Dimension Reduction for Classification with Gene Expression Microarray Data , 2006, Statistical applications in genetics and molecular biology.

[7]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[8]  William Wells,et al.  Detection of prostate cancer by integration of line-scan diffusion, T2-mapping and T2-weighted magnetic resonance imaging; a multichannel statistical classifier. , 2003, Medical physics.

[9]  Jae Won Lee,et al.  An extensive comparison of recent classification tools applied to microarray data , 2004, Comput. Stat. Data Anal..

[10]  J. McNeal,et al.  Regional morphology and pathology of the prostate. , 1968, American journal of clinical pathology.

[11]  Sayan Mukherjee,et al.  Supervised Dimension Reduction Using Bayesian Mixture Modeling , 2010, AISTATS.

[12]  Thomas Hambrock,et al.  Computerized analysis of prostate lesions in the peripheral zone using dynamic contrast enhanced MRI. , 2008, Medical physics.

[13]  Jelle O. Barentsz,et al.  Local staging of prostate cancer using magnetic resonance imaging: a meta-analysis , 2002, European Radiology.

[14]  L. Breiman.  Arcing classifier (with discussion and a rejoinder by the author) , 1998 .

[15]  Hiroyuki Yoshida,et al.  Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps , 2001, IEEE Transactions on Medical Imaging.

[16]  H. Dang,et al.  Prostate cancer detection: comparison of t2-weighted imaging, diffusion-weighted imaging, proton magnetic resonance spectroscopic imaging, and the three techniques combined , 2008, Acta radiologica.

[17]  Serge J. Belongie,et al.  Normalized cuts in 3-D for spinal MRI segmentation , 2004, IEEE Transactions on Medical Imaging.

[18]  C. Read,et al.  Handbook of the normal distribution , 1982 .

[19]  Joshua B. Tenenbaum,et al.  The Isomap Algorithm and Topological Stability , 2002, Science.

[20]  Thomas G. Dietterich.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[21]  R. Cohen,et al.  Transition zone carcinoma of the prostate gland: a common indolent tumour type that occasionally manifests aggressive behaviour , 2003, Pathology.

[22]  Trygve Randen,et al.  Filtering for Texture Classification: A Comparative Study , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Neil D. Lawrence,et al.  Spectral Dimensionality Reduction via Maximum Entropy , 2011, AISTATS.

[24]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[25]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[26]  J. Epstein,et al.  Interobserver reproducibility of Gleason grading of prostatic carcinoma: general pathologist. , 2001, Human pathology.

[27]  Chen Lin,et al.  Neural Information Processing -letters and Reviews Simplify Support Vector Machines by Iterative Learning , 2022 .

[28]  J. M. Hans du Buf,et al.  A review of recent texture segmentation and feature extraction techniques , 1993 .

[29]  R. Lenkinski,et al.  Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 tesla endorectal, in vivo T2‐weighted MR imagery , 2012, Journal of magnetic resonance imaging : JMRI.

[30]  Tim W. Nattkemper,et al.  Visual exploratory analysis of DCE-MRI data in breast cancer by dimensional data reduction: A comparative study , 2006, Biomed. Signal Process. Control..

[31]  N Karssemeijer,et al.  Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis , 2012, Physics in medicine and biology.

[32]  Kazuro Sugimura,et al.  Prostate cancer detection with 3 T MRI: Comparison of diffusion‐weighted imaging and dynamic contrast‐enhanced MRI in combination with T2‐weighted imaging , 2010, Journal of magnetic resonance imaging : JMRI.

[33]  Namkug Kim,et al.  Apparent diffusion coefficient: Prostate cancer versus noncancerous tissue according to anatomical region , 2008, Journal of magnetic resonance imaging : JMRI.

[34]  Anant Madabhushi,et al.  A consensus embedding approach for segmentation of high resolution in vivo prostate magnetic resonance imagery , 2008, SPIE Medical Imaging.

[35]  Zhuowen Tu,et al.  Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[36]  Evis Sala,et al.  Transition zone prostate cancers: features, detection, localization, and staging at endorectal MR imaging. , 2006, Radiology.

[37]  Anant Madabhushi,et al.  Enhanced multi-protocol analysis via intelligent supervised embedding (EMPrAvISE): detecting prostate cancer on multi-parametric MRI , 2011, Medical Imaging.

[38]  Anant Madabhushi,et al.  A Comprehensive Segmentation, Registration, and Cancer Detection Scheme on 3 Tesla In VivoProstate DCE-MRI , 2008, MICCAI.

[39]  Louise C. Showe,et al.  Bioinformatics Original Paper Combining Multi-species Genomic Data for Microrna Identification Using a Naı¨ve Bayes Classifier , 2022 .

[40]  Jacek M. Zurada,et al.  Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images , 1996, IEEE Trans. Medical Imaging.

[41]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[42]  A. Villers,et al.  Prostate cancer characterization on MR images using fractal features. , 2010, Medical physics.

[43]  David R. Karger,et al.  Tackling the Poor Assumptions of Naive Bayes Text Classifiers , 2003, ICML.

[44]  Theofanis Sapatinas,et al.  Discriminant Analysis and Statistical Pattern Recognition , 2005 .

[45]  Peter J. Bickel,et al.  Maximum Likelihood Estimation of Intrinsic Dimension , 2004, NIPS.

[46]  Michalis Vazirgiannis,et al.  c ○ 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. On Clustering Validation Techniques , 2022 .

[47]  Gert R. G. Lanckriet,et al.  Classification of a large microarray data set: algorithm comparison and analysis of drug signatures. , 2005, Genome research.

[48]  Byung Kwan Park,et al.  Update of Prostate Magnetic Resonance Imaging at 3 T , 2008, Journal of computer assisted tomography.

[49]  H. Hricak,et al.  Imaging of prostate cancer. , 2007, Radiologic clinics of North America.

[50]  Vipul Sharma.  MR Imaging of the Abdomen and Pelvis , 2011 .

[51]  Robert M. Nishikawa,et al.  A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications , 2005, IEEE Transactions on Medical Imaging.

[52]  Lawrence K. Saul,et al.  Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold , 2003, J. Mach. Learn. Res..

[53]  Ana L. N. Fred,et al.  Combining multiple clusterings using evidence accumulation , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[54]  Yongyi Yang,et al.  Supervised and unsupervised methods for prostate cancer segmentation with multispectral MRI. , 2010, Medical physics.

[55]  Joshua B. Tenenbaum,et al.  Global Versus Local Methods in Nonlinear Dimensionality Reduction , 2002, NIPS.

[56]  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.

[57]  Anant Madabhushi,et al.  Automated computer-derived prostate volumes from MR imaging data: comparison with radiologist-derived MR imaging and pathologic specimen volumes. , 2012, Radiology.

[58]  Anant Madabhushi,et al.  Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data , 2012, BMC Bioinformatics.

[59]  R. B. Potts.  Some generalized order-disorder transformations , 1952, Mathematical Proceedings of the Cambridge Philosophical Society.

[60]  Zhou Cheng,et al.  Prostate cancer detection: comparison of T2-weighted imaging, diffusion-weighted imaging, proton magnetic resonance spectroscopy, and the combination of three techniques , 2010 .

[61]  Anant Madabhushi,et al.  Determining histology-MRI slice correspondences for defining MRI-based disease signatures of prostate cancer , 2011, Comput. Medical Imaging Graph..

[62]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[63]  R E Lenkinski,et al.  Current role of MR imaging in the staging of adenocarcinoma of the prostate. , 1993, Radiology.

[64]  Nello Cristianini,et al.  Kernel-Based Data Fusion and Its Application to Protein Function Prediction in Yeast , 2003, Pacific Symposium on Biocomputing.

[65]  Thomas Hambrock,et al.  Computer-assisted analysis of peripheral zone prostate lesions using T2-weighted and dynamic contrast enhanced T1-weighted MRI , 2010, Physics in medicine and biology.

[66]  Haitao Zhao.  Combining labeled and unlabeled data with graph embedding , 2006, Neurocomputing.

[67]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[68]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[69]  J. Cardoso,et al.  Blind beamforming for non-gaussian signals , 1993 .

[70]  Pietro Perona,et al.  Self-Tuning Spectral Clustering , 2004, NIPS.

[71]  Hongbin Zha,et al.  Riemannian Manifold Learning , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[72]  Anant Madabhushi,et al.  An active learning based classification strategy for the minority class problem: application to histopathology annotation , 2011, BMC Bioinformatics.

[73]  Tao Li,et al.  A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression , 2004, Bioinform..

[74]  Zhi-Hua Zhou,et al.  Supervised nonlinear dimensionality reduction for visualization and classification , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[75]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[76]  Dimitris N. Metaxas,et al.  Automated detection of prostatic adenocarcinoma from high-resolution ex vivo MRI , 2005, IEEE Transactions on Medical Imaging.

[77]  A. Evans,et al.  MRI simulation-based evaluation of image-processing and classification methods , 1999, IEEE Transactions on Medical Imaging.

[78]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[79]  R. E. Kent.  Robert E , 2005 .

[80]  David G. Stork,et al.  Pattern Classification , 1973 .

[81]  Feiping Nie,et al.  Multiple view semi-supervised dimensionality reduction , 2010, Pattern Recognit..

[82]  George Lee,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.

[83]  Nassir Navab,et al.  Manifold Learning for Image-Based Breathing Gating with Application to 4D Ultrasound , 2010, MICCAI.

[84]  I. Menchi,et al.  Local staging of prostate carcinoma with endorectal coil MRI: correlation with whole-mount radical prostatectomy specimens , 2004, European Radiology.

[85]  Vassili A. Kovalev,et al.  3D Texture Analysis of MRI Brain Datasets , 2001, IEEE Trans. Medical Imaging.

[86]  T. Podder,et al.  A real-time prostate cancer detection technique using needle insertion force and patient-specific criteria during percutaneous intervention. , 2009, Medical physics.

[87]  B. Nicolas Bloch,et al.  The role of magnetic resonance imaging (MRI) in prostate cancer imaging and staging at 1.5 and 3 Tesla: the Beth Israel Deaconess Medical Center (BIDMC) approach. , 2008, Cancer biomarkers : section A of Disease markers.

[88]  R. Lenkinski,et al.  Accurate prostate volume estimation using multifeature active shape models on T2-weighted MRI. , 2011, Academic radiology.

[89]  M. Naderi.  Think globally... , 2004, HIV prevention plus!.

[90]  H M Pollack,et al.  Use of endorectal surface coil magnetic resonance imaging for local staging of prostate cancer. , 1993, The Journal of urology.

[91]  Kevin Dawson,et al.  Sample phenotype clusters in high-density oligonucleotide microarray data sets are revealed using Isomap, a nonlinear algorithm , 2005, BMC Bioinformatics.

[92]  Larry S. Davis,et al.  Improved fast gauss transform and efficient kernel density estimation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[93]  John C. Russ,et al.  The Image Processing Handbook , 2016, Microscopy and Microanalysis.

[94]  Aytekin Oto,et al.  Prostate cancer: differentiation of central gland cancer from benign prostatic hyperplasia by using diffusion-weighted and dynamic contrast-enhanced MR imaging. , 2010, Radiology.

[95]  Eric Bauer,et al.  An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.

[96]  G. F. Hughes,et al.  On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.

[97]  Shao Chao,et al.  P-ISOMAP:A New ISOMAP-Based Data Visualization Algorithm with Less Sensitivity to the Neighborhood Size , 2006 .

[98]  Jarkko Venna,et al.  Local multidimensional scaling , 2006, Neural Networks.

[99]  Nicolas Le Roux,et al.  Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering , 2003, NIPS.

[100]  Nassir Navab,et al.  Manifold Learning for Multi-Modal Image Registration , 2010, BMVC.

[101]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[102]  Matti Pietikäinen,et al.  Selection of the Optimal Parameter value for the Locally Linear Embedding Algorithm , 2002, FSKD.

[103]  D. Opitz,et al.  Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..

[104]  M. Cree,et al.  A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms , 1998, Comput. Biol. Medicine.

[105]  C. Mountford,et al.  Proton magnetic resonance spectroscopy of the central, transition and peripheral zones of the prostate: assignments and correlation with histopathology , 2008, Magnetic Resonance Materials in Physics, Biology and Medicine.

[106]  Dit-Yan Yeung,et al.  Robust locally linear embedding , 2006, Pattern Recognit..

[107]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[108]  J R Thornbury,et al.  Local staging of prostate cancer with endorectal MR imaging: correlation with histopathology. , 1996, AJR. American journal of roentgenology.

[109]  Masoom A. Haider,et al.  Prostate Cancer Localization With Multispectral MRI Using Cost-Sensitive Support Vector Machines and Conditional Random Fields , 2010, IEEE Transactions on Image Processing.

[110]  M. Rifkin,et al.  Magnetic Resonance Imaging of Prostate Cancer: Update , 1995, Topics in magnetic resonance imaging : TMRI.

[111]  Geoffrey E. Hinton,et al.  Comparing Classification Methods for Longitudinal fMRI Studies , 2010, Neural Computation.

[112]  Jianbo Shi,et al.  Comparing Ensembles of Learners: Detecting Prostate Cancer from High Resolution MRI , 2006, CVAMIA.

[113]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[114]  L Axel,et al.  Prostatic carcinoma and benign prostatic hyperplasia: correlation of high-resolution MR and histopathologic findings. , 1989, Radiology.

[115]  Anant Madabhushi,et al.  Consensus-Locally Linear Embedding (C-LLE): Application to Prostate Cancer Detection on Magnetic Resonance Spectroscopy , 2008, MICCAI.

[116]  Carla E. Brodley,et al.  Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach , 2003, ICML.

[117]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[118]  P Tiwari,et al.  Multimodal wavelet embedding representation for data combination (MaWERiC): integrating magnetic resonance imaging and spectroscopy for prostate cancer detection , 2012, NMR in biomedicine.

[119]  Huiqing Liu,et al.  A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns. , 2002, Genome informatics. International Conference on Genome Informatics.

[120]  Jan Sijbers,et al.  Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft‐tissue tumors in T1‐MRI images , 2010, Journal of magnetic resonance imaging : JMRI.

[121]  Matti Pietikäinen,et al.  Supervised Locally Linear Embedding , 2003, ICANN.

[122]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[123]  Jayaram K. Udupa,et al.  Generalized scale: Theory, algorithms, and application to image inhomogeneity correction , 2006, Comput. Vis. Image Underst..

[124]  Wilson S. Geisler,et al.  Multichannel Texture Analysis Using Localized Spatial Filters , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[125]  CHRISTOPH BUSCH,et al.  Wavelet based texture segmentation of multi-modal tomographic images , 1997, Comput. Graph..

[126]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[127]  Purang Abolmaesumi,et al.  High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models , 2010, Medical Image Anal..

[128]  Wei-Yin Loh,et al.  A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms , 2000, Machine Learning.

[129]  E. M. Wright,et al.  Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.

[130]  Anant Madabhushi,et al.  Spectral Embedding Based Probabilistic Boosting Tree (ScEPTre): Classifying High Dimensional Heterogeneous Biomedical Data , 2009, MICCAI.

[131]  Kar-Ann Toh,et al.  An empirical comparison of nine pattern classifiers , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[132]  F. Cendes,et al.  Texture analysis of medical images. , 2004, Clinical radiology.

[133]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[134]  Denis Larocque,et al.  An empirical comparison of ensemble methods based on classification trees , 2003 .

[135]  S. Dudoit,et al.  Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data , 2002 .

[136]  P. Carroll,et al.  Multiparametric magnetic resonance imaging in prostate cancer: present and future , 2008, Current opinion in urology.

[137]  E. Sumers.  Staging prostate cancer with MR imaging. , 1993, Radiology.

[138]  Foster Provost,et al.  The effect of class distribution on classifier learning: an empirical study , 2001 .

[139]  Eric Peltier,et al.  Evaluation of the interobserver reproducibility of Gleason grading of prostatic adenocarcinoma using tissue microarrays. , 2003, Human pathology.

[140]  Anthony V D'Amico,et al.  Accurate Gleason grading of prostatic adenocarcinoma in prostate needle biopsies by general pathologists. , 2003, Archives of pathology & laboratory medicine.

[141]  Wensheng Guo,et al.  Computer aided automatic detection of malignant lesions in diffuse optical mammography. , 2010, Medical physics.

[142]  S. Moss,et al.  A UK‐based investigation of inter‐ and intra‐observer reproducibility of Gleason grading of prostatic biopsies , 2006, Histopathology.

[143]  Anant Madabhushi,et al.  COLLINARUS: collection of image-derived non-linear attributes for registration using splines , 2009, Medical Imaging.

[144]  Jayaram K. Udupa,et al.  New methods of MR image intensity standardization via generalized scale , 2005, SPIE Medical Imaging.

[145]  L. Breiman.  Arcing Classifiers , 1998 .

[146]  Herbert Y. Kressel,et al.  Prostatic carcinoma: staging with MR imaging at 1.5 T. , 1988 .

[147]  George Kollios,et al.  BoostMap: An Embedding Method for Efficient Nearest Neighbor Retrieval , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[148]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[149]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[150]  Ludmila I. Kuncheva,et al.  Using Diversity with Three Variants of Boosting: Aggressive, Conservative, and Inverse , 2002, Multiple Classifier Systems.

[151]  William M. Wells,et al.  Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation , 2004, IEEE Transactions on Medical Imaging.

[152]  David Ward,et al.  Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data , 2003, Bioinform..

[153]  Hongmin Cai,et al.  Multiparametric tissue characterization of brain neoplasms and their recurrence using pattern classification of MR images. , 2008, Academic radiology.

[154]  Jing Fang,et al.  Computerized characterization of prostate cancer by fractal analysis in MR images , 2009, Journal of magnetic resonance imaging : JMRI.

[155]  R. Lenkinski,et al.  Elastic registration of multimodal prostate MRI and histology via multiattribute combined mutual information. , 2011, Medical physics.

[156]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[157]  W. Marsden.  I and J , 2012 .

[158]  Rich Caruana,et al.  An empirical comparison of supervised learning algorithms , 2006, ICML.

[159]  E Le Rumeur,et al.  Comparison of automated and visual texture analysis in MRI: characterization of normal and diseased skeletal muscle. , 1999, Magnetic resonance imaging.

[160]  Jianbo Shi,et al.  Graph Embedding to Improve Supervised Classification and Novel Class Detection: Application to Prostate Cancer , 2005, MICCAI.