Janowczyk, A., Basavanhally, A., & Madabhushi, A.(2016).Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology.Computerized Medical Imaging and Graphics [08956111].
Penzias, G., Janowczyk, A., Singanamalli, A., Rusu, M., Shih, N., Feldman, M., Stricker, P., Delprado, W., Tiwari, S., Böhm, M., Haynes, A., Ponsky, L., Viswanath, S. E., & Madabhushi, A. E.(2016).AutoStitcher: An Automated Program for Efficient and Robust Reconstruction of Digitized Whole Histological Sections from Tissue Fragments.Scientific Reports [20452322],6
Janowczyk, A., Doyle, S., Gilmore, H., & Madabhushi, A.(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 [21681163].
Cohn, H., Lu, C., Paspulati, R., Katz, J., Madabhushi, A., Stein, S., Cominelli, F., Viswanath, S. E., & Dave, M. E.(2016).Tu1966 A Machine-Learning Based Risk Score to Predict Response to Therapy in Crohn's Disease via Baseline MRE.Gastroenterology [00165085],150(4).
Romo-Bucheli, D., Janowczyk, A., Romero, E., Gilmore, H., & Madabhushi, A.(2016).Automated tubule nuclei quantification and correlation with oncotype DX risk categories in ER+ breast cancer whole slide images.SPIE Medical Imaging [Conference],9791
Litjens, G., Elliott, R., Shih, N., Feldman, M., Kobus, T., De Hulsbergen-van Kaa, C., Barentsz, J., Huisman, H., & Madabhushi, A.(2016).Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging..Radiology,278(1),135-45.
Tiwari, P., Prasanna, P., Wolansky, L., Pinho, M., Cohen, M., Nayate, A., Gupta, A., Singh, G., Hatanpaa, K., Sloan, A., Rogers, L., & Madabhushi, A.(2016).Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study.American Journal of Neuroradiology [01956108],37(12),2231-2236.
Madabhushi, A., & Lee, G.(2016).Image analysis and machine learning in digital pathology: Challenges and opportunities.Medical Image Analysis [13618415],33, 170-175.
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 [19326203],11(4).
Xue, Z., 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 [09252312],191, 214-223.
Li, L., Rusu, M., Viswanath, S. E., Penzias, G. E., Pahwa, S. E., Gollamudi, J. E., & Madabhushi, A. E.(2016).Multi-modality registration via multi-scale textural and spectral embedding representations.SPIE Medical Imaging [Conference],9784
Karabalin, R., Rimm, D., Ganesan, S., & Madabhushi, A.(2016).Abstract P5-07-12: Local nuclear architecture features from H&E images predict early versus distant recurrence in lymph node negative, ER+ breast cancers.Cancer Research [00085472],76(4 Supplement),P5-07-12-P5-07-12.
Madabhushi, A., Ginsburg, S., & Lee, G.(2016).Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology.IEEE Transactions on Medical Imaging,35(1),76 - 88.
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 [20452322],6
Singanamalli, A., Rusu, M., Sparks, R., Shih, N., Ziober, A., Wang, Y., Tomaszewski, J., Rosen, M., Feldman, M., & Madabhushi, A.(2016).Identifying in vivo DCE MRI markers associated with microvessel architecture and gleason grades of prostate cancer..Journal of magnetic resonance imaging : JMRI,43(1),149-58.
Ginsburg, S., Lee, G., Karabalin, R., & Madabhushi, A.(2016).Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology..IEEE transactions on medical imaging,35(1),76-88.
Janowczyk, A., & Madabhushi, A.(2016).Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases.Journal of Pathology Informatics [21533539],7(1).
Gawlik, A., Lee, G., Whitney, J., Epstein, J., Veltri, R., & Madabhushi, A.(2016).MP02-17 COMPUTER EXTRACTED NUCLEAR FEATURES FROM FEULGEN AND H&E IMAGES PREDICT BIOCHEMICAL RECURRENCE IN PROSTATE CANCER PATIENTS FOLLOWING RADICAL PROSTATECTOMY.The Journal of Urology [00225347],195(4),e16-e17.
Ginsburg, S., Algohary, A., Pahwa, S., Gulani, V., Ponsky, L., Aronen, H., Boström, P., Böhm, M., Haynes, A., Brenner, P., Delprado, W., Thompson, J., Pulbrock, M., Taimen, P., Villani, R., Stricker, P., Rastinehad, A., Jambor, I., & Madabhushi, A.(2016).Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study: Radiomic Features for Prostate Cancer Detection on MRI.Journal of Magnetic Resonance Imaging [10531807].
Xu, J., Xiang, L., Wang, P., Ganesan, S., Feldman, M., Shih, N., Gilmore, H., & Madabhushi, A.(2015).Sparse Non-negative Matrix Factorization (SNMF) based color unmixing for breast histopathological image analysis..Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society,46 Pt 1, 20-9.
Tiwari, P., Danish, S., 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.
Rusu, M., Golden, T., Wang, Z., Gow, A., & Madabhushi, A.(2015).Framework for 3D histologic reconstruction and fusion with in vivo MRI: Preliminary results of characterizing pulmonary inflammation in a mouse model..Medical physics,42(8),4822-32.
