Home
Center for Computational Imaging and Personalized Diagnostics

Publications

Export 152 results:
2021
Peyster, EG, Arabyarmohammadi S, Janowczyk A, Azarianpour-Esfahani S, Sekulic M, Cassol C, Blower L, Parwani A, Lal P, Feldman MD et al..  2021.  An automated computational image analysis pipeline for histological grading of cardiac allograft rejection.. European heart journal. Abstract
Khorrami, MH, Bera K, Thawani R, Rajiah P, Gupta A, Fu P, Linden P, Pennell N, Jacono F, Gilkeson RC et al..  2021.  Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans.. European journal of cancer (Oxford, England : 1990). 148:146-158. Abstract
Atta-Fosu, T, LaBarbera M, Ghose S, Schoenhagen P, Saliba W, Tchou PJ, Lindsay BD, Desai MY, Kwon D, Chung MK et al..  2021.  A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT.. BMC medical imaging. 21(1):45. Abstract
Firouznia, M, Feeny AK, LaBarbera MA, McHale M, Cantlay C, Kalfas N, Schoenhagen P, Saliba W, Tchou P, Barnard J et al..  2021.  Machine Learning-derived Fractal Features of Shape and Texture of the Left Atrium and Pulmonary Veins from Cardiac CT Scans are Associated with Risk of Recurrence of Atrial Fibrillation Post-ablation.. Circulation. Arrhythmia and electrophysiology. Abstract
Leo, P, Chandramouli S, Farré X, Elliott R, Janowczyk A, Bera K, Fu P, Janaki N, El-Fahmawi A, Shahait M et al..  2021.  Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2.. European urology focus. Abstract
Eck, B, Chirra PV, Muchhala A, Hall S, Bera K, Tiwari P, Madabhushi A, Seiberlich N, Viswanath SE.  2021.  Prospective Evaluation of Repeatability and Robustness of Radiomic Descriptors in Healthy Brain Tissue Regions In Vivo Across Systematic Variations in T2-Weighted Magnetic Resonance Imaging Acquisition Parameters.. Journal of magnetic resonance imaging : JMRI. Abstract
Hiremath, A, Shiradkar R, Fu P, Mahran A, Rastinehad AR, Tewari A, Tirumani S, Purysko A, Ponsky L, Madabhushi A.  2021.  An integrated nomogram combining deep learning, Prostate Imaging-Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study.. The Lancet. Digital health. 3(7):e445-e454. Abstract
Koyuncu, CF, Lu C, Bera K, Zhang Z, Xu J, Toro P, Corredor G, Chute D, Fu P, Thorstad WL et al..  2021.  Computerized tumor multinucleation index (MuNI) is prognostic in p16+ oropharyngeal carcinoma.. The Journal of clinical investigation. 131(8) Abstract
Liu, JTC, Glaser AK, Bera K, True LD, Reder NP, Eliceiri KW, Madabhushi A.  2021.  Harnessing non-destructive 3D pathology.. Nature biomedical engineering. 5(3):203-218. Abstract
2020
Yan, C, Nakane K, Wang X, Fu Y, Lu H, Fan X, Feldman MD, Madabhushi A, Xu J.  2020.  Automated gleason grading on prostate biopsy slides by statistical representations of homology profile.. Computer methods and programs in biomedicine. 194:105528. Abstract
Vaidya, P, Bera K, Patil PD, Gupta A, Jain P, Alilou M, Khorrami MH, Velcheti V, Madabhushi A.  2020.  Novel, non-invasive imaging approach to identify patients with advanced non-small cell lung cancer at risk of hyperprogressive disease with immune checkpoint blockade.. Journal for immunotherapy of cancer. 8(2) Abstract
Lu, C, Koyuncu C, Corredor G, Prasanna P, Leo P, Wang X, Janowczyk A, Bera K, Lewis J, Velcheti V et al..  2020.  Feature-driven local cell graph (FLocK): New computational pathology-based descriptors for prognosis of lung cancer and HPV status of oropharyngeal cancers.. Medical image analysis. 68:101903. Abstract
Verma, R, Correa R, Hill VB, Statsevych V, Bera K, Beig N, Mahammedi A, Madabhushi A, Ahluwalia M, Tiwari P.  2020.  Tumor Habitat-derived Radiomic Features at Pretreatment MRI That Are Prognostic for Progression-free Survival in Glioblastoma Are Associated with Key Morphologic Attributes at Histopathologic Examination: A Feasibility Study.. Radiology. Artificial intelligence. 2(6):e190168. Abstract
Alvarez-Jimenez, C, Antunes JT, Talasila N, Bera K, Brady JT, Gollamudi J, Marderstein E, Kalady MF, Purysko A, Willis JE et al..  2020.  Radiomic Texture and Shape Descriptors of the Rectal Environment on Post-Chemoradiation T2-Weighted MRI are Associated with Pathologic Tumor Stage Regression in Rectal Cancers: A Retrospective, Multi-Institution Study.. Cancers. 12(8) Abstract
Li, L, Shiradkar R, Leo P, Algohary A, Fu P, Tirumani S, Mahran A, Buzzy C, Obmann VC, Mansoori B et al..  2020.  A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI.. EBioMedicine. 63:103163. Abstract
Sadri, AR, Janowczyk A, Zhou R, Verma R, Beig N, Antunes J, Madabhushi A, Tiwari P, Viswanath SE.  2020.  Technical Note: MRQy - An open-source tool for quality control of MR imaging data.. Medical physics. 47(12):6029-6038. Abstract
Algohary, A, Shiradkar R, Pahwa S, Purysko A, Verma S, Moses D, Shnier R, Haynes A-M, Delprado W, Thompson J et al..  2020.  Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study.. Cancers. 12(8) Abstract
Zhao, Z, Bian Y, Jiang H, Fang X, Li J, Cao K, Ma C, Wang L, Zheng J, Yue X et al..  2020.  CT-Radiomic Approach to Predict G1/2 Nonfunctional Pancreatic Neuroendocrine Tumor.. Academic radiology. 27(12):e272-e281. Abstract
Lu, C, Bera K, Wang X, Prasanna P, Xu J, Janowczyk A, Beig N, Yang M, Fu P, Lewis J et al..  2020.  A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study.. The Lancet. Digital health. 2(11):e594-e606. Abstract
Bera, K, Katz I, Madabhushi A.  2020.  Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology.. JCO clinical cancer informatics. 4:1039-1050. Abstract
Bhargava, HK, Leo P, Elliott R, Janowczyk A, Whitney J, Gupta S, Fu P, Yamoah K, Khani F, Robinson BD et al..  2020.  Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients.. Clinical cancer research : an official journal of the American Association for Cancer Research. 26(8):1915-1923. Abstract
Madabhushi, A, Feldman MD, Leo P.  2020.  Deep-learning approaches for Gleason grading of prostate biopsies.. The Lancet. Oncology. 21(2):187-189.
Khorrami, MH, Prasanna P, Gupta A, Patil P, Velu PD, Thawani R, Corredor G, Alilou M, Bera K, Fu P et al..  2020.  Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non-Small Cell Lung Cancer.. Cancer immunology research. 8(1):108-119. Abstract
Ismail, M, Hill V, Statsevych V, Mason E, Correa R, Prasanna P, Singh G, Bera K, Thawani R, Ahluwalia M et al..  2020.  Can Tumor Location on Pre-treatment MRI Predict Likelihood of Pseudo-Progression vs. Tumor Recurrence in Glioblastoma?-A Feasibility Study. Frontiers in computational neuroscience. 14:563439. Abstract
2019
Khorrami, MH, Khunger M, Zagouras A, Patil P, Thawani R, Bera K, Rajiah P, Fu P, Velcheti V, Madabhushi A.  2019.  Combination of Peri- and Intratumoral Radiomic Features on Baseline CT Scans Predicts Response to Chemotherapy in Lung Adenocarcinoma.. Radiology. Artificial intelligence. 1(2):e180012. Abstract
Purysko, AS, Magi-Galluzzi C, Mian OY, Sittenfeld S, Davicioni E, du Plessis M, Buerki C, Bullen J, Li L, Madabhushi A et al..  2019.  Correlation between MRI phenotypes and a genomic classifier of prostate cancer: preliminary findings.. European radiology. Abstract
Corredor, G, Wang X, Zhou Y, Lu C, Fu P, Syrigos K, Rimm DL, Yang M, Romero E, Schalper KA et al..  2019.  Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non-Small Cell Lung Cancer.. Clinical cancer research : an official journal of the American Association for Cancer Research. 25(5):1526-1534. Abstract
Beig, N, Khorrami MH, Alilou M, Prasanna P, Braman N, Orooji M, Rakshit S, Bera K, Rajiah P, Ginsberg J et al..  2019.  Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas.. Radiology. 290(3):783-792. Abstract
Van Es, SL, Madabhushi A.  2019.  The revolving door for AI and pathologists-docendo discimus? Journal of medical artificial intelligence. 2
Bui, MM, Riben MW, Allison KH, Chlipala E, Colasacco C, Kahn AG, Lacchetti C, Madabhushi A, Pantanowitz L, Salama ME et al..  2019.  Quantitative Image Analysis of Human Epidermal Growth Factor Receptor 2 Immunohistochemistry for Breast Cancer: Guideline From the College of American Pathologists.. Archives of pathology & laboratory medicine. Abstract
Xu, J, Gong L, Wang G, Lu C, Gilmore H, Zhang S, Madabhushi A.  2019.  Convolutional neural network initialized active contour model with adaptive ellipse fitting for nuclear segmentation on breast histopathological images.. Journal of medical imaging (Bellingham, Wash.). 6(1):017501. Abstract
Prasanna, P, Mitra J, Beig N, Nayate A, Patel J, Ghose S, Thawani R, Partovi S, Madabhushi A, Tiwari P.  2019.  Mass Effect Deformation Heterogeneity (MEDH) on Gadolinium-contrast T1-weighted MRI is associated with decreased survival in patients with right cerebral hemisphere Glioblastoma: A feasibility study.. Scientific reports. 9(1):1145. Abstract
Vamathevan, J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, Li B, Madabhushi A, Shah P, Spitzer M et al..  2019.  Applications of machine learning in drug discovery and development.. Nature reviews. Drug discovery. Abstract
Braman, N, Prasanna P, Whitney J, Singh S, Beig N, Etesami M, Bates DDB, Gallagher K, Bloch NB, Vulchi M et al..  2019.  Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer.. JAMA network open. 2(4):e192561. Abstract
Braman, N, Prasanna P, Whitney J, Singh S, Beig N, Etesami M, Bates DDB, Gallagher K, Bloch NB, Vulchi M et al..  2019.  Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer.. JAMA network open. 2(4):e192561. Abstract
Janowczyk, A, Zuo R, Gilmore H, Feldman M, Madabhushi A.  2019.  HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides.. JCO clinical cancer informatics. 3:1-7. Abstract
Chirra, P, Leo P, Yim M, Bloch NB, Rastinehad AR, Purysko A, Rosen M, Madabhushi A, Viswanath SE.  2019.  Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI.. Journal of medical imaging (Bellingham, Wash.). 6(2):024502. Abstract
Prasanna, P, Karnawat A, Ismail M, Madabhushi A, Tiwari P.  2019.  Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging.. Journal of medical imaging (Bellingham, Wash.). 6(2):024005. Abstract
Bera, K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A.  2019.  Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology.. Nature reviews. Clinical oncology. 16(11):703-715. Abstract
Li, H, Whitney J, Bera K, Gilmore H, Thorat MA, Badve S, Madabhushi A.  2019.  Quantitative nuclear histomorphometric features are predictive of Oncotype DX risk categories in ductal carcinoma in situ: preliminary findings.. Breast cancer research : BCR. 21(1):114. Abstract
Khorrami, MH, Jain P, Bera K, Alilou M, Thawani R, Patil P, Ahmad U, Murthy S, Stephans K, Fu P et al..  2019.  Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features.. Lung cancer (Amsterdam, Netherlands). 135:1-9. Abstract
Feeny, AK, Rickard J, Patel D, Toro S, Trulock KM, Park CJ, LaBarbera MA, Varma N, Niebauer MJ, Sinha S et al..  2019.  Machine Learning Prediction of Response to Cardiac Resynchronization Therapy: Improvement Versus Current Guidelines.. Circulation. Arrhythmia and electrophysiology. 12(7):e007316. Abstract
Vamathevan, J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, Li B, Madabhushi A, Shah P, Spitzer M et al..  2019.  Applications of machine learning in drug discovery and development.. Nature reviews. Drug discovery. 18(6):463-477. Abstract
Braman, N, Prasanna P, Whitney J, Singh S, Beig N, Etesami M, Bates DDB, Gallagher K, Bloch NB, Vulchi M et al..  2019.  Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer.. JAMA network open. 2(4):e192561. Abstract
Janowczyk, A, Zuo R, Gilmore H, Feldman M, Madabhushi A.  2019.  HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides.. JCO clinical cancer informatics. 3:1-7. Abstract
Viswanath, SE, Chirra PV, Yim MC, Rofsky NM, Purysko AS, Rosen MA, Bloch NB, Madabhushi A.  2019.  Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study.. BMC medical imaging. 19(1):22. Abstract
Merisaari, H, Taimen P, Shiradkar R, Ettala O, Pesola M, Saunavaara J, Boström PJ, Madabhushi A, Aronen HJ, Jambor I.  2019.  Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer.. Magnetic resonance in medicine. 83(6):2293-2309. Abstract
2018
Carleton, NM, Lee G, Madabhushi A, Veltri RW.  2018.  Advances in the computational and molecular understanding of the prostate cancer cell nucleus.. Journal of cellular biochemistry. 119(9):7127-7142. Abstract
Alilou, M, Orooji M, Beig N, Prasanna P, Rajiah P, Donatelli C, Velcheti V, Rakshit S, Yang M, Jacono F et al..  2018.  Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas.. Scientific reports. 8(1):15290. Abstract
Leo, P, Elliott R, Shih NNC, Gupta S, Feldman M, Madabhushi A.  2018.  Stable and discriminating features are predictive of cancer presence and Gleason grade in radical prostatectomy specimens: a multi-site study.. Scientific reports. 8(1):14918. Abstract
Lu, C, Romo-Bucheli D, Wang X, Janowczyk A, Ganesan S, Gilmore H, Rimm D, Madabhushi A.  2018.  Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers.. Laboratory investigation; a journal of technical methods and pathology. 98(11):1438-1448. Abstract
Whitney, J, Corredor G, Janowczyk A, Ganesan S, Doyle S, Tomaszewski J, Feldman M, Gilmore H, Madabhushi A.  2018.  Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.. BMC cancer. 18(1):610. Abstract
Bera, K, Velcheti V, Madabhushi A.  2018.  Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications.. American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting. 38:1008-1018. Abstract
Antunes, J, Viswanath S, Brady JT, Crawshaw B, Ros P, Steele S, Delaney CP, Paspulati R, Willis J, Madabhushi A.  2018.  Coregistration of Preoperative MRI with Ex Vivo Mesorectal Pathology Specimens to Spatially Map Post-treatment Changes in Rectal Cancer Onto In Vivo Imaging: Preliminary Findings.. Academic radiology. Abstract
Beig, N, Patel J, Prasanna P, Hill V, Gupta A, Correa R, Bera K, Singh S, Partovi S, Varadan V et al..  2018.  Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma.. Scientific reports. 8(1):7. Abstract
Thawani, R, McLane M, Beig N, Ghose S, Prasanna P, Velcheti V, Madabhushi A.  2018.  Radiomics and radiogenomics in lung cancer: A review for the clinician.. Lung cancer (Amsterdam, Netherlands). 115:34-41. Abstract
Algohary, A, Viswanath S, Shiradkar R, Ghose S, Pahwa S, Moses D, Jambor I, Shnier R, Böhm M, Haynes A-M et al..  2018.  Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings.. Journal of magnetic resonance imaging : JMRI. Abstract
Shiradkar, R, Ghose S, Jambor I, Taimen P, Ettala O, Purysko AS, Madabhushi A.  2018.  Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings.. Journal of magnetic resonance imaging : JMRI. 48(6):1626-1636. Abstract
Orooji, M, Alilou M, Rakshit S, Beig N, Khorrami MH, Rajiah P, Thawani R, Ginsberg J, Donatelli C, Yang M et al..  2018.  Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography.. Journal of medical imaging (Bellingham, Wash.). 5(2):024501. Abstract
Peyster, EG, Madabhushi A, Margulies KB.  2018.  Advanced Morphologic Analysis for Diagnosing Allograft Rejection: The Case of Cardiac Transplant Rejection.. Transplantation. 102(8):1230-1239. Abstract
Hipp, JD, Johann DJ, Chen Y, Madabhushi A, Monaco J, Cheng J, Rodriguez-Canales J, Stumpe MC, Riedlinger G, Rosenberg AZ et al..  2018.  Computer-Aided Laser Dissection: A Microdissection Workflow Leveraging Image Analysis Tools.. Journal of pathology informatics. 9:45. Abstract
Nirschl, JJ, Janowczyk A, Peyster EG, Frank R, Margulies KB, Feldman MD, Madabhushi A.  2018.  A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue.. PloS one. 13(4):e0192726. Abstract
Cruz-Roa, A, Gilmore H, Basavanhally A, Feldman M, Ganesan S, Shih N, Tomaszewski J, Madabhushi A, González F.  2018.  High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection.. PloS one. 13(5):e0196828. Abstract
Penzias, G, Singanamalli A, Elliott R, Gollamudi J, Shih N, Feldman M, Stricker PD, Delprado W, Tiwari S, Böhm M et al..  2018.  Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings.. PloS one. 13(8):e0200730. Abstract
Janowczyk, A, Doyle S, Gilmore H, Madabhushi A.  2018.  A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images.. Computer methods in biomechanics and biomedical engineering. Imaging & visualization. 6(3):270-276. Abstract
2017
Wang, H, Viswanath S, Madabhushi A.  2017.  Discriminative Scale Learning (DiScrn): Applications to Prostate Cancer Detection from MRI and Needle Biopsies.. Scientific reports. 7(1):12375. Abstract
Wang, X, Janowczyk A, Zhou Y, Thawani R, Fu P, Schalper K, Velcheti V, Madabhushi A.  2017.  Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images.. Scientific reports. 7(1):13543. Abstract
Ghose, S, Shiradkar R, Rusu M, Mitra J, Thawani R, Feldman M, Gupta AC, Purysko AS, Ponsky L, Madabhushi A.  2017.  Prostate shapes on pre-treatment MRI between prostate cancer patients who do and do not undergo biochemical recurrence are different: Preliminary Findings.. Scientific reports. 7(1):15829. Abstract
Martel, AL, Hosseinzadeh D, Senaras C, Zhou Y, Yazdanpanah A, Shojaii R, Patterson ES, Madabhushi A, Gurcan MN.  2017.  An Image Analysis Resource for Cancer Research: PIIP-Pathology Image Informatics Platform for Visualization, Analysis, and Management.. Cancer research. 77(21):e83-e86. Abstract
Braman, NM, Etesami M, Prasanna P, Dubchuk C, Gilmore H, Tiwari P, Plecha D, Madabhushi A.  2017.  Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI.. Breast cancer research : BCR. 19(1):57. Abstract
Alilou, M, Beig N, Orooji M, Rajiah P, Velcheti V, Rakshit S, Reddy N, Yang M, Jacono F, Gilkeson RC et al..  2017.  An integrated segmentation and shape based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT.. Medical physics. Abstract
Viswanath, SE, Tiwari P, Lee G, Madabhushi A.  2017.  Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: concepts, workflow, and use-cases.. BMC medical imaging. 17(1):2. Abstract
Kim, JJ, Bennett NK, Devita MS, Chahar S, Viswanath S, Lee EA, Jung G, Shao PP, Childers EP, Liu S et al..  2017.  Optical High Content Nanoscopy of Epigenetic Marks Decodes Phenotypic Divergence in Stem Cells.. Scientific reports. 7:39406. Abstract
Romo-Bucheli, D, Janowczyk A, Gilmore H, Romero E, Madabhushi A.  2017.  A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers.. Cytometry. Part A : the journal of the International Society for Analytical Cytology. Abstract
Rusu, M, Purysko AS, Verma S, Kiechle J, Gollamudi J, Ghose S, Herrmann K, Gulani V, Paspulati R, Ponsky L et al..  2017.  Computational imaging reveals shape differences between normal and malignant prostates on MRI.. Scientific reports. 7:41261. Abstract
Li, L, Pahwa S, Penzias G, Rusu M, Gollamudi J, Viswanath S, Madabhushi A.  2017.  Co-Registration of ex vivo Surgical Histopathology and in vivo T2 weighted MRI of the Prostate via multi-scale spectral embedding representation.. Scientific reports. 7(1):8717. Abstract
Singanamalli, A, Wang H, Madabhushi A.  2017.  Cascaded Multi-view Canonical Correlation (CaMCCo) for Early Diagnosis of Alzheimer's Disease via Fusion of Clinical, Imaging and Omic Features.. Scientific reports. 7(1):8137. Abstract
Lu, C, Lewis JS, Dupont WD, Plummer DW, Janowczyk A, Madabhushi A.  2017.  An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival.. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc. Abstract
Cruz-Roa, A, Gilmore H, Basavanhally A, Feldman M, Ganesan S, Shih NNC, Tomaszewski J, González FA, Madabhushi A.  2017.  Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent.. Scientific reports. 7:46450. Abstract
Rusu, M, Rajiah P, Gilkeson R, Yang M, Donatelli C, Thawani R, Jacono FJ, Linden P, Madabhushi A.  2017.  Co-registration of pre-operative CT with ex vivo surgically excised ground glass nodules to define spatial extent of invasive adenocarcinoma on in vivo imaging: a proof-of-concept study.. European radiology. Abstract
Xu, J, Monaco JP, Sparks R, Madabhushi A.  2017.  Connecting Markov random fields and active contour models: application to gland segmentation and classification.. Journal of medical imaging (Bellingham, Wash.). 4(2):021107. Abstract
Gurcan, MN, Tomaszewski JE, Madabhushi A.  2017.  Special Section Guest Editorial: Digital Pathology.. Journal of medical imaging (Bellingham, Wash.). 4(2):021101.
Janowczyk, A, Basavanhally A, Madabhushi A.  2017.  Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology.. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society. 57:50-61. Abstract
Corredor, G, Whitney J, Arias V, Madabhushi A, Romero E.  2017.  Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features.. Journal of medical imaging (Bellingham, Wash.). 4(2):021105. Abstract
Bektik, E, Dennis A, Prasanna P, Madabhushi A, Fu J-D.  2017.  Single cell qPCR reveals that additional HAND2 and microRNA-1 facilitate the early reprogramming progress of seven-factor-induced human myocytes.. PloS one. 12(8):e0183000. Abstract
2016
Xu, J, Luo X, Wang G, Gilmore H, Madabhushi A.  2016.  A Deep Convolutional Neural Network for Segmenting and Classifying Epithelial and Stromal Regions in Histopathological Images. Neurocomputing. 191:223.
Romo-Bucheli, D, Janowczyk A, Gilmore H, Romero E, Madabhushi A.  2016.  Automated Tubule Nuclei Quantification and Correlation with Oncotype DX risk categories in ER+ Breast Cancer Whole Slide Images.. Scientific reports. 6:32706. Abstract
Prasanna, P, Patel J, Partovi S, Madabhushi A, Tiwari P.  2016.  Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings.. European radiology. Abstract
Prasanna, P, Patel J, Partovi S, Madabhushi A, Tiwari P.  2016.  Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings.. European radiology. Abstract
Lu, C, Xu H, Xu J, Gilmore H, Mandal M, Madabhushi A.  2016.  Multi-Pass Adaptive Voting for Nuclei Detection in Histopathological Images.. Scientific reports. 6:33985. Abstract
Leo, P, Lee G, Shih NNC, Elliott R, Feldman MD, Madabhushi A.  2016.  Evaluating stability of histomorphometric features across scanner and staining variations: prostate cancer diagnosis from whole slide images.. Journal of medical imaging (Bellingham, Wash.). 3(4):047502. Abstract
Leo, P, Lee G, Shih NNC, Elliott R, Feldman MD, Madabhushi A.  2016.  Evaluating stability of histomorphometric features across scanner and staining variations: prostate cancer diagnosis from whole slide images.. Journal of medical imaging (Bellingham, Wash.). 3(4):047502. Abstract
Madabhushi, A, Lee G.  2016.  Image analysis and machine learning in digital pathology: Challenges and opportunities.. Medical image analysis. 33:170-5. Abstract
Madabhushi, A, Lee G.  2016.  Image analysis and machine learning in digital pathology: Challenges and opportunities.. Medical image analysis. 33:170-5. Abstract
Shiradkar, R, Podder TK, Algohary A, Viswanath S, Ellis RJ, Madabhushi A.  2016.  Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI.. Radiation oncology (London, England). 11(1):148. Abstract
Janowczyk, A, Basavanhally A, Madabhushi A.  2016.  Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology.. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society. Abstract
Ginsburg, SB, Taimen P, Merisaari H, Vainio P, Boström PJ, Aronen HJ, Jambor I, Madabhushi A.  2016.  Patient-specific pharmacokinetic parameter estimation on dynamic contrast-enhanced MRI of prostate: Preliminary evaluation of a novel AIF-free estimation method.. Journal of magnetic resonance imaging : JMRI. Abstract
Sparks, R, Madabhushi A.  2016.  Out-of-Sample Extrapolation utilizing Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval for Histopathology Images.. Scientific reports. 6:27306. Abstract
Penzias, G, Janowczyk A, Singanamalli A, Rusu M, Shih N, Feldman M, Stricker PD, Delprado W, Tiwari S, Böhm M et al..  2016.  AutoStitcher: An Automated Program for Efficient and Robust Reconstruction of Digitized Whole Histological Sections from Tissue Fragments.. Scientific reports. 6:29906. Abstract
Bhargava, R, Madabhushi A.  2016.  Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology.. Annual review of biomedical engineering. 18:387-412. Abstract
Wan, T, Bloch NB, Plecha D, Thompson CIL, Gilmore H, Jaffe C, Harris L, Madabhushi A.  2016.  A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting OncotypeDX Risk Scores.. Scientific reports. 6:21394. Abstract
Ginsburg, SB, Algohary A, Pahwa S, Gulani V, Ponsky L, Aronen HJ, Boström PJ, Böhm M, Haynes A-M, Brenner P et al..  2016.  Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study.. Journal of magnetic resonance imaging : JMRI. Abstract
Ginsburg, SB, Taimen P, Merisaari H, Vainio P, Boström PJ, Aronen HJ, Jambor I, Madabhushi A.  2016.  Patient-specific pharmacokinetic parameter estimation on dynamic contrast-enhanced MRI of prostate: Preliminary evaluation of a novel AIF-free estimation method.. Journal of magnetic resonance imaging : JMRI. 44(6):1405-1414. Abstract
Antunes, J, Viswanath S, Rusu M, Valls L, Hoimes C, Avril N, Madabhushi A.  2016.  Radiomics Analysis on FLT-PET/MRI for Characterization of Early Treatment Response in Renal Cell Carcinoma: A Proof-of-Concept Study.. Translational oncology. 9(2):155-62. Abstract
Lee, G, Romo Bucheli DE, Madabhushi A.  2016.  Adaptive Dimensionality Reduction with Semi-Supervision (AdDReSS): Classifying Multi-Attribute Biomedical Data.. PloS one. 11(7):e0159088. Abstract
Janowczyk, A, Madabhushi A.  2016.  Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases.. Journal of pathology informatics. 7:29. Abstract
Toth, R, Sperling D, Madabhushi A.  2016.  Quantifying Post- Laser Ablation Prostate Therapy Changes on MRI via a Domain-Specific Biomechanical Model: Preliminary Findings.. PloS one. 11(4):e0150016. Abstract
Lee, G, Veltri RW, Zhu G, Ali S, Epstein JI, Madabhushi A.  2016.  Nuclear Shape and Architecture in Benign Fields Appear to Predict Biochemical Recurrence in Prostate Cancer Patients Following Radical Prostatectomy: Preliminary Findings. European Radiology.
Janowczyk, A, Madabhushi A, Doyle S, Gilmore H.  2016.  A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization.
2015
Tiwari, P, Danish SF, Jiang B, Madabhushi A.  2015.  Association of computerized texture features on MRI with early treatment response following laser ablation for neuropathic cancer pain: preliminary findings.. Journal of medical imaging (Bellingham, Wash.). 2(4):041008. Abstract
Varadan, V, Kamalakaran S, Gilmore H, Banerjee N, Janevski A, Miskimen KLS, Williams N, Basavanhalli A, Madabhushi A, Lezon-Geyda K et al..  2015.  Brief-exposure to preoperative bevacizumab reveals a TGF-β signature predictive of response in HER2-negative breast cancers.. International journal of cancer. Journal international du cancer. Abstract
Rusu, M, Golden T, Wang H, Gow A, Madabhushi A.  2015.  Framework for 3D histologic reconstruction and fusion with in vivo MRI. Medical Physics. 42:4822. Abstract
Litjens, GJS, Elliott R, Shih NNC, Feldman MD, Kobus T, Hulsbergen-van de Kaa C, Barentsz JO, Huisman HJ, Madabhushi A.  2015.  Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging.. Radiology. :142856. Abstract
Ginsburg, S, Lee G, Ali S, Madabhushi A.  2015.  Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology.. IEEE transactions on medical imaging. Abstract
Xu, J, Xiang L, Liu Q, Gilmore H, Wu J, Tang J, Madabhushi A.  2015.  Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology images.. IEEE transactions on medical imaging. Abstract
Sridhar, A, Doyle S, Madabhushi A.  2015.  Content-based image retrieval of digitized histopathology in boosted spectrally embedded spaces.. Journal of pathology informatics. 6:41. Abstract
Singanamalli, A, Rusu M, Sparks RE, Shih NNC, Ziober A, Wang L-P, Tomaszewski J, Rosen M, Feldman M, Madabhushi A.  2015.  Identifying in vivo DCE MRI markers associated with microvessel architecture and gleason grades of prostate cancer.. Journal of magnetic resonance imaging : JMRI. Abstract
Basavanhally, A, Viswanath S, Madabhushi A.  2015.  Predicting classifier performance with limited training data: applications to computer-aided diagnosis in breast and prostate cancer.. PloS one. 10(5):e0117900. Abstract
Sparks, R, Nicolas Bloch B, Feleppa E, Barratt D, Moses D, Ponsky L, Madabhushi A.  2015.  Multiattribute probabilistic prostate elastic registration (MAPPER): Application to fusion of ultrasound and magnetic resonance imaging.. Medical physics. 42(3):1153. Abstract
Veta, M, van Diest PJ, Willems SM, Wang H, Madabhushi A, Cruz-Roa A, Gonzalez F, Larsen ABL, Vestergaard JS, Dahl AB et al..  2015.  Assessment of algorithms for mitosis detection in breast cancer histopathology images.. Medical image analysis. 20(1):237-48. Abstract
Cruz-Roa, A, Arévalo J, Basavanhally A, Madabhushi A, González F.  2015.  A comparative evaluation of supervised and unsupervised representation learning approaches for anaplastic medulloblastoma differentiation. SPIE Medical Imaging. 9287:92870G.
2014
Viswanath, S, Toth R, Rusu M, Sperling D, Lepor H, Futterer J, Madabhushi A.  2014.  Identifying quantitative in vivo multi-parametric MRI features for treatment related changes after laser interstitial thermal therapy of prostate cancer. Neurocomputing. 144:13–23.
Lee, G, Singanamalli A, Wang H, Feldman M, Master S, Shih N, Spangler E, Rebbeck T, Tomaszewski J, Madabhushi A.  2014.  Supervised Multi-View Canonical Correlation Analysis (sMVCCA): Integrating histologic and proteomic features for predicting recurrent prostate cancer.. IEEE transactions on medical imaging. 34(1):284-97. Abstract
Wang, H, Cruz-Roa A, Basavanhally A, Gilmore H, Shih N, Feldman M, Tomaszewski J, Gonzalez F, Madabhushi A.  2014.  Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features.. Journal of medical imaging (Bellingham, Wash.). 1(3):034003. Abstract
Colen, R, Foster I, Gatenby R, Giger ME, Gillies R, Gutman D, Heller M, Jain R, Madabhushi A, Madhavan S et al..  2014.  NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures.. Translational oncology. 7(5):556-69. Abstract
Ali, S, Veltri R, Epstein JI, Christudass C, Madabhushi A.  2014.  Selective invocation of shape priors for deformable segmentation and morphologic classification of prostate cancer tissue microarrays.. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society. 14:00176-1. Abstract
Ginsburg, SB, Viswanath SE, Bloch NB, Rofsky NM, Genega EM, Lenkinski RE, Madabhushi A.  2014.  Novel PCA-VIP scheme for ranking MRI protocols and identifying computer-extracted MRI measurements associated with central gland and peripheral zone prostate tumors.. Journal of magnetic resonance imaging : JMRI. :1383-93. Abstract
Rusu, M, Bloch NB, Jaffe CC, Genega EM, Lenkinski RE, Rofsky NM, Feleppa E, Madabhushi A.  2014.  Prostatome: a combined anatomical and disease based MRI atlas of the prostate.. Medical physics. 41(7):072301. Abstract
Prasanna, P, Tiwari P, Madabhushi A.  2014.  Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): Distinguishing Tumor Confounders and Molecular Subtypes on MRI. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). 8675:73-80.
Litjens, G, Huisman H, Elliott R, Shih N, Feldman M, Viswanath S, Fütterer J, Bomers J, Madabhushi A.  2014.  Distinguishing benign confounding treatment changes from residual prostate cancer on MRI following laser ablation. SPIE Medical Imaging. 9036:90361D-90361D-12.
Tiwari, P, Danish S, Madabhushi A.  2014.  Identifying MRI Markers Associated with Early Response following Laser Ablation for Neurological Disorders: Preliminary Findings.. PloS one. 9(12):e114293. Abstract
Rusu, M, Kurhanewicz J, Tewari A, Madabhushi A.  2014.  A prostate MRI atlas of biochemical failures following cancer treatment. SPIE Medical Imaging. 9035:903513-903513-10.
Wang, H, Singanamalli A, Ginsburg S, Madabhushi A.  2014.  Selecting Features with Group-Sparse Nonnegative Supervised Canonical Correlation Analysis (GNCCA): Multimodal Prostate Cancer Prognosis. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). 8675:385-392.
Orooji, M, Sparks R, Bloch NB, Feleppa E, Barratt D, Madabhushi A.  2014.  Spatially aware expectation maximization (SpAEM): application to prostate TRUS segmentation. SPIE Medical Imaging. 9034:90343Y-90343Y-13.
Hwuang, E, Rusu M, Karthigeyan S, Agner SC, Sparks R, Shih N, Tomaszewski JE, Rosen M, Feldman M, Madabhushi A.  2014.  Spectral embedding-based registration (SERg) for multimodal fusion of prostate histology and MRI. SPIE Medical Imaging. 9034:90343P-90343P-15.
Tiwari, P, Prasanna P, Rogers L, Wolansky L, Badve C, Sloan A, Cohen M, Madabhushi A.  2014.  Texture descriptors to distinguish radiation necrosis from recurrent brain tumors on multi-parametric MRI. SPIE Medical Imaging. 9035:90352B-90352B-12.
Toth, R, Traughber B, Ellis R, Kurhanewicz J, Madabhushi A.  2014.  A Domain Constrained Deformable (DoCD) Model for Co-registration of Pre- and Post-Radiated Prostate MRI.. Neurocomputing. 114:3-12. Abstract
Wan, T, Bloch NB, Danish S, Madabhushi A.  2014.  A Learning Based Fiducial-driven Registration Scheme for Evaluating Laser Ablation Changes in Neurological Disorders.. Neurocomputing. 144:24-37. Abstract
Cruz-Roa, A, Xu J, Madabhushi A.  2014.  A note on the stability and discriminability of graph based features for classification problems in digital pathology.. SPIE Medical Imaging.
Litjens, G, Huisman H, Elliot R, Shih N, Feldman M, Viswanath SE, Futterer J, Bomers J, Madabhushi A.  2014.  Quantitative identification of magnetic resonance imaging features of prostate cancer response following laser ablation and radical prostatectomy.. Journal of Medical Imaging. 1(3):035001.
Agner, SC, Rosen MA, Englander S, Tomaszewski JE, Feldman MD, Zhang P, Mies C, Schnall MD, Madabhushi A.  2014.  Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: a feasibility study.. Radiology. 272(1):91-99. Abstract
Lee, G, Sparks R, Ali S, Shih NNC, Feldman MD, Spangler E, Rebbeck T, Tomaszewski JE, Madabhushi A.  2014.  Co-occurring gland angularity in localized subgraphs: predicting biochemical recurrence in intermediate-risk prostate cancer patients.. PloS one. 9(5):e97954. Abstract
Wan, T, Madabhushi A, Phinikaridou A, Hamilton JA, Hua N, Pham T, Danagoulian J, Kleiman R, Buckler AJ.  2014.  Spatio-temporal texture (SpTeT) for distinguishing vulnerable from stable atherosclerotic plaque on dynamic contrast enhancement (DCE) MRI in a rabbit model.. Medical physics. 41(4):042303. Abstract
Cruz-Roa, A, Basavanhally A, González F, Gilmore H, Feldman M, Ganesan S, Shih N, Tomaszewski J, Madabhushi A.  2014.  Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. SPIE Medical Imaging. 9041:904103-904103-15.
Wang, H, Cruz-Roa A, Basavanhally A, Gilmore H, Shih N, Feldman M, Tomaszewski J, Gonzalez F, Madabhushi A.  2014.  Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection. SPIE Medical Imaging. 9041:90410B-90410B-10.
Ginsburg, SB, Rusu M, Kurhanewicz J, Madabhushi A.  2014.  Computer extracted texture features on T2w MRI to predict biochemical recurrence following radiation therapy for prostate cancer. SPIE Medical Imaging. 9035:903509-903509-13.
Toth, RJ, Shih N, Tomaszewski JE, Feldman MD, Kutter O, Yu DN, Paulus JC, Paladini G, Madabhushi A.  2014.  Histostitcher™: An informatics software platform for reconstructing whole-mount prostate histology using the extensible imaging platform framework.. Journal of pathology informatics. 5:8. Abstract
Tiwari, P, Danish S, Madabhushi A.  2014.  Identifying MRI markers to evaluate early treatment-related changes post-laser ablation for cancer pain management. SPIE Medical Imaging. 9036:90362L-90362L-11.
Singanamalli, A, Wang H, Lee G, Shih N, Rosen M, Master S, Tomaszewski J, Feldman M, Madabhushi A.  2014.  Supervised multi-view canonical correlation analysis: fused multimodal prediction of disease diagnosis and prognosis. SPIE Medical Imaging. 9038:903805-903805-13.
Litjens, G, Toth R, van de Ven W, Hoeks C, Kerkstra S, van Ginneken B, Vincent G, Guillard G, Birbeck N, Zhang J et al..  2014.  Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge.. Medical image analysis. 18(2):359-73. Abstract
2013
Ali, S, Lewis JS, Madabhushi A.  2013.  Spatially Aware Cell Cluster(SpACCl) Graphs: Predicting Outcome in Oropharyngeal p16+ Tumors. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). 8149:412-419.
2012
Bulman, J, Toth R, Patel A, Bloch NB, McMahon CJ, Ngo L, Madabhushi A, Rofsky NM.  2012.  Automated Computer-derived Prostate Volumes from MR Imaging Data: Comparison with Radiologist-derived MR Imaging and Pathologic Specimen Volumes.. Radiology. 262(1):144-51. Abstract

 *IEEE COPYRIGHT NOTICE: 1997 IEEE. * Personal use of this material is permitted. However, permission to reprint/ republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

*COPYRIGHT NOTICE:* These materials are presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.