Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer

[1]  Anant Madabhushi,et al.  A Boosted Bayesian Multiresolution Classifier for Prostate Cancer Detection From Digitized Needle Biopsies , 2012, IEEE Transactions on Biomedical Engineering.

[2]  John R. Gilbertson,et al.  Computer aided diagnostic tools aim to empower rather than replace pathologists: Lessons learned from computational chess , 2011, Journal of pathology informatics.

[3]  A. Madabhushi,et al.  Integrated diagnostics: a conceptual framework with examples , 2010, Clinical chemistry and laboratory medicine.

[4]  Chronic Disease Division Cancer facts and figures , 2010 .

[5]  Gyan Bhanot,et al.  Computerized Image-Based Detection and Grading of Lymphocytic Infiltration in HER2+ Breast Cancer Histopathology , 2010, IEEE Transactions on Biomedical Engineering.

[6]  A. Madabhushi,et al.  Histopathological Image Analysis: A Review , 2009, IEEE Reviews in Biomedical Engineering.

[7]  Dionisis Cavouras,et al.  Improving accuracy in astrocytomas grading by integrating a robust least squares mapping driven support vector machine classifier into a two level grade classification scheme , 2008, Comput. Methods Programs Biomed..

[8]  Mikhail Teverovskiy,et al.  Multifeature Prostate Cancer Diagnosis and Gleason Grading of Histological Images , 2007, IEEE Transactions on Medical Imaging.

[9]  R. A. Zoroofi,et al.  An image analysis approach for automatic malignancy determination of prostate pathological images , 2007, Cytometry. Part B, Clinical cytometry.

[10]  Kim L. Boyer,et al.  IMAGE ANALYSIS FOR AUTOMATED ASSESSMENT OF GRADE OF NEUROBLASTIC DIFFERENTIATION , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[11]  Timothy F. Cootes,et al.  Assessing the accuracy of non-rigid registration with and without ground truth , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[12]  Isabelle Meiers,et al.  Prostate biopsy and optimization of cancer yield. , 2006, European urology.

[13]  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.

[14]  L. Egevad,et al.  The 2005 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma , 2005, The American journal of surgical pathology.

[15]  James Diamond,et al.  The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia. , 2004, Human pathology.

[16]  Constantine Katsinis,et al.  Automated identification of microstructures on histology slides , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[17]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[18]  Bayan S. Sharif,et al.  Fractal analysis in the detection of colonic cancer images , 2002, IEEE Transactions on Information Technology in Biomedicine.

[19]  A. Ruifrok,et al.  Quantification of histochemical staining by color deconvolution. , 2001, Analytical and quantitative cytology and histology.

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

[21]  P. Houts,et al.  ACS cancer facts and figures , 2000 .

[22]  Leen-Kiat Soh,et al.  Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices , 1999, IEEE Trans. Geosci. Remote. Sens..

[23]  John R. Gilbertson,et al.  Evaluation of prostate tumor grades by content-based image retrieval , 1999, Other Conferences.

[24]  B Weyn,et al.  Automated breast tumor diagnosis and grading based on wavelet chromatin texture description. , 1998, Cytometry.

[25]  J. Epstein,et al.  Partial atrophy in prostate needle cores: another diagnostic pitfall for the surgical pathologist. , 1998, The American journal of surgical pathology.

[26]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

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

[28]  P. Walsh,et al.  Clinical and cost impact of second-opinion pathology. Review of prostate biopsies prior to radical prostatectomy. , 1996, The American journal of surgical pathology.

[29]  Fernand Meyer,et al.  Topographic distance and watershed lines , 1994, Signal Process..

[30]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[31]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[32]  J. Ross Quinlan,et al.  Decision trees and decision-making , 1990, IEEE Trans. Syst. Man Cybern..

[33]  V. Gold Compendium of chemical terminology , 1987 .

[34]  S. Beucher Use of watersheds in contour detection , 1979 .

[35]  N. Otsu A threshold selection method from gray level histograms , 1979 .

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

[37]  Gleason Df Classification of prostatic carcinomas. , 1966 .

[38]  D. Gleason Classification of prostatic carcinomas. , 1966, Cancer chemotherapy reports.