Digital pathology image analysis: opportunities and challenges.

This is the fixed version of an article made available by an organization that acts as a publisher by formally and exclusively declaring the article "published". If it is an "early release" article (formally identified as being published even before the compilation of a volume issue and assignment of associated metadata), it is citable via some permanent identifier(s), and final copy-editing, proof corrections, layout, and typesetting have been applied.

[1]  Godfried T. Toussaint,et al.  The relative neighbourhood graph of a finite planar set , 1980, Pattern Recognit..

[2]  S Friedman,et al.  The importance of histologic grade in long-term prognosis of breast cancer: a study of 1,010 patients, uniformly treated at the Institut Gustave-Roussy. , 1987, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[3]  M. Brawer,et al.  Quantitative morphometric analysis of the microcirculation in prostate carcinoma , 1992, Journal of cellular biochemistry. Supplement.

[4]  D. Bostwick.  Grading prostate cancer. , 1994, American journal of clinical pathology.

[5]  D. Bostwick,et al.  Gleason grading of prostatic needle biopsies. Correlation with grade in 316 matched prostatectomies. , 1994, The American journal of surgical pathology.

[6]  P H Bartels,et al.  COMPUTERIZED SCENE SEGMENTATION FOR THE DISCRIMINATION OF ARCHITECTURAL FEATURES IN DUCTAL PROLIFERATIVE LESIONS OF THE BREAST , 1997, The Journal of pathology.

[7]  Nicholas Ayache,et al.  The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration , 1998, MICCAI.

[8]  Leslie W Dalton,et al.  Histologic Grading of Breast Cancer: Linkage of Patient Outcome with Level of Pathologist Agreement , 2000, Modern Pathology.

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

[10]  T. Namiki,et al.  Discrepancies between Gleason scores of needle biopsy and radical prostatectomy specimens , 2001, Pathology international.

[11]  I. Ellis,et al.  Implications of pathologist concordance for breast cancer assessments in mammography screening from age 40 years. , 2002, Human pathology.

[12]  L. Egevad,et al.  Interobserver reproducibility of percent Gleason grade 4/5 in total prostatectomy specimens. , 2002, The Journal of urology.

[13]  Pranab Dey,et al.  Fractal dimensions of breast lesions on cytology smears , 2003, Diagnostic cytopathology.

[14]  Hamid Soltanian-Zadeh,et al.  Multiwavelet grading of pathological images of prostate , 2003, IEEE Transactions on Biomedical Engineering.

[15]  Mahul B Amin,et al.  Update on the Gleason Grading System for Prostate Cancer: Results of an International Consensus Conference of Urologic Pathologists , 2006, Advances in anatomic pathology.

[16]  Anant Madabhushi,et al.  A Boosting Cascade for Automated Detection of Prostate Cancer from Digitized Histology , 2006, MICCAI.

[17]  Constantine Katsinis,et al.  Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer , 2006, BMC Medical Imaging.

[18]  Lennart Franzén,et al.  How well does the Gleason score predict prostate cancer death? A 20-year followup of a population based cohort in Sweden. , 2006, The Journal of urology.

[19]  A. Madabhushi,et al.  Detecting Prostatic Adenocarcinoma From Digitized Histology Using a Multi-Scale Hierarchical Classification Approach , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[21]  Jun Kong,et al.  Computerized Pathological Image Analysis For Neuroblastoma Prognosis , 2007, AMIA.

[22]  Paolo Napoletano,et al.  A multiresolution diffused expectation-maximization algorithm for medical image segmentation , 2007, Comput. Biol. Medicine.

[23]  R. Engers.  Reproducibility and reliability of tumor grading in urological neoplasms , 2007, World Journal of Urology.

[24]  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.

[25]  Metin Nafi Gürcan,et al.  Adaptive Discriminant Wavelet Packet Transform and Local Binary Patterns for Meningioma Subtype Classification , 2008, MICCAI.

[26]  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.

[27]  Olcay Sertel,et al.  Computer-assisted grading of neuroblastic differentiation. , 2008, Archives of pathology & laboratory medicine.

[28]  M. Rubin,et al.  Interobserver reproducibility of Gleason grading: evaluation using prostate cancer tissue microarrays , 2008, Journal of Cancer Research and Clinical Oncology.

[29]  Purang Abolmaesumi,et al.  Detection of Prostate Cancer from Whole-Mount Histology Images Using Markov Random Fields , 2008 .

[30]  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.

[31]  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.

[32]  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.

[33]  Joel H. Saltz,et al.  Stroma classification for neuroblastoma on graphics processors , 2009, Int. J. Data Min. Bioinform..

[34]  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.