Algohary, A., Viswanath, S. E., Shiradkar, R. E., Ghose, S. E., Pahwa, S. E., Moses, D. E., Jambor, I. E., Shnier, R. E., B�hm, M. E., Haynes, A. E., Brenner, P. E., Delprado, W. E., Thompson, J. E., Pulbrock, M. E., Purysko, A. E., Verma, S. E., Ponsky, L. E., Stricker, P. E., & Madabhushi, A. E.(2018).Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings: Radiomics Categorizes PCa Patients on AS.Journal of Magnetic Resonance Imaging.
Slipchenko, M., Whitney, J., Thawani, R., Gilmore, H., Badve, S., & Madabhushi, A.(2018).Abstract P4-09-12: Quantitative image features of nuclear and tubule architecture distinguish high and low oncotype DX risk categories of ductal carcinoma in situ from H&E tissue images.Cancer Research,78(4 Supplement),P4-09-12-P4-09-12.
Whitney, J., Romeo-Bucheli, D., Janowczyk, A., Ganesan, S., Feldman, M., Gilmore, H., & Madabhushi, A.(2018).Abstract P4-09-11: Computer extracted features of tumor grade from H&E images predict oncotype DX risk categories for early stage ER+ breast cancer.Cancer Research,78(4 Supplement),P4-09-11-P4-09-11.
Antunes, J., Viswanath, S. E., Brady, J. E., Crawshaw, B. E., Ros, P. E., Steele, S. E., Delaney, C. E., Paspulati, R. E., Willis, J. E., & Madabhushi, A. E.(2018).Coregistration of Preoperative MRI with Ex Vivo Mesorectal Pathology Specimens to Spatially Map Post-treatment Changes in Rectal Cancer Onto In Vivo Imaging.Academic Radiology.
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,115, 34-41.
Ravichandran, K., Braman, N., Janowczyk, A., & Madabhushi, A.(2018).A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI.,10575
Leo, P., Shankar, E., Elliott, R., Janowczyk, A., Madabhushi, A., & Gupta, S.(2018).Combination of nuclear NF-κB/p65 localization and gland morphological features from surgical specimens is predictive of early biochemical recurrence in prostate cancer patients.,10581
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).
Ghose, S., Shiradkar, R., Rusu, M., Mitra, J., Thawani, R., Feldman, M., Gupta, A., Purysko, A., 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).
Madabhushi, A., Ghose, S., Shiradkar, R., & Rajat, T.(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.
Madabhushi, A., & Zhou, Y.(2017).An Image Analysis Resource for Cancer Research: PIIP-Pathology Image Informatics Platform for Visualization, Analysis, and Management.Cancer Research,77(21),e83 - 386.
Rusu, M., Thawani, R., & 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.European Radiology,27(10),4209 - 4217.
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,91(6),566-573.
Madabhushi, A., Braman, N., Prasanna, P., & Tiwari, P.(2017).Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI.Breast Cancer Research,19(1),57.
Corredor-Prada, G., Madabhushi, A., & Whitney, J.(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),020015.
Janowczyk, A., Basavanhally, A., & Madabhushi, A.(2017).Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology.Computerized Medical Imaging and Graphics,57, 50-61.
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.
Alilou, M., Beig, N., Orooji, M., Rajiah, P., Velcheti, V., Rakshit, S., Reddy, S., Yang, M., Jacono, F., Gilkeson, R., Linden, P., & Madabhushi, A.(2017).An integrated segmentation and shape based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT..Medical physics.
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.
Rusu, M., Purysko, A., Verma, S., Kiechle, J., Gollamudi, J., Ghose, S., Herrmann, K., Gulani, V., Paspulati, R., Ponsky, L., Böhm, M., Haynes, A., Moses, D., Shnier, R., Delprado, W., Thompson, J., Stricker, P., & Madabhushi, A.(2017).Computational imaging reveals shape differences between normal and malignant prostates on MRI..Scientific reports,7, 41261.
Nirschl, J., Janowczyk, A., Peyster, E., Frank, R., Margulies, K., Feldman, M., & Madabhushi, A.(2017).Deep Learning Tissue Segmentation in Cardiac Histopathology Images..
Viswanath, S. E., Tiwari, P. E., Lee, G. E., & Madabhushi, A. E.(2017).Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: concepts, workflow, and use-cases..BMC medical imaging,17(1),2.
Kim, J., Bennett, N., Devita, M., Chahar, S., Viswanath, S. E., Lee, H. E., Jung, H. E., Shao, P. E., Childers, E. E., Liu, C. E., Kulesa, A. E., Garcia, B. E., Becker, M. E., Hwang, W. E., Madabhushi, A. E., Verzi, M. E., & Moghe, P. E.(2017).Optical High Content Nanoscopy of Epigenetic Marks Decodes Phenotypic Divergence in Stem Cells..Scientific reports,7, 39406.
