Sevgi, D., Srivastava, S., Whitney, J., O’Connell, M., Sil Kar, S., Hu, M., Reese, J., Madabhushi, A., & Ehlers, J.(2021).Characterization of Ultra-widefield Angiographic Vascular Features in Diabetic 2 Retinopathy with Automated Severity Classification.Ophthalmology Science.
Miao, R., Toth, R., Zhou, Y., Madabhushi, A., & Janowczyk, A.(2021).Quick Annotator: an open-source digital pathology based rapid image annotation tool.The Journal of Pathology: Clinical Research.
Sil Kar, S., Sevgi, D., Dong, V., Srivastava, S., Madabhushi, A., & Ehlers, J.(2021).Multi-Compartment Spatially-derived Radiomics from Optical Coherence Tomography Predict Anti-VEGF Treatment Durability in Macular Edema Secondary to Retinal Vascular Disease: Preliminary Findings.IEEE Journal of Translational Engineering in Health and Medicine.
Hiremath, A., Shiradkar, R., Fu, P., Mahran, A., Rastinehad, A., 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.
Schömig-Markiefka , B., Pryalukhin, A., Hulla, W., Bychkov, A., Fukuoka, J., Madabhushi, A., Achter, V., Nieroda, L., Büttner, R., Quaas, A., & Tolkach, Y.(2021).Quality control stress test for deep learning-based diagnostic model in digital pathology.Modern Pathology.
Peyster, E., Arabyarmohammadi , S., Janowczyk, A., Azarianpour-Esfahani, S., Sekulic, M., Cassol, C., Blower, L., Parwani, A., Lal, P., Feldman, M., Margulies, K., & Madabhushi, A.(2021).An automated computational image analysis pipeline for histological grading of cardiac allograft rejection.European Heart Journal,42(24),2356-2369.
Lu, C., Koyuncu, C., Janowczyk, A., Griffith, C., Chute, D., Lewis, J., & Madabhushi, A.(2021).Deep Learning-Based Cancer Region Segmentation from H&E Slides for HPV-Related Oropharyngeal Squamous Cell Carcinomas.Springer International Publishing.
Alilou, M., Prasanna, P., Bera, K., Gupta, A., Rajiah, P., Yang, M., Jacono, F., Velcheti, V., Gilkeson, R., Linden, P., & Madabhushi, A.(2021).A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans.Cancers,13(11),2781.
Leo, P., Janowczyk, A., Elliott, R., Janaki, N., Bera, K., Shiradkar, R., Farre, X., Fu, P., El-Fahmawi, A., Shahait, M., Kim, J., Lee, D., Yamoah, K., Rebbeck, T., Khani, F., Robinson, B., Eklund, L., Jambor, I., Merisaari, H., , O., Taimen, P., Aronen, H., Boström, P., Tewari, A., Magi-Galluzzi, C., Klein, E., Purysko, A., Shin, N., Feldman, M., Gupta, S., Lai, P., & Madabhushi, A.(2021).Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study.NPJ Precision Oncology,5(1),35.
Khorrami, M., Bera, K., Thawani, R., Rajiah, P., Gupta, A., Fu, P., Linden, P., Pennell, N., Jacono, F., Gilkeson, R., Velchetii, V., & Madabhushi, A.(2021).Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans.European Journal of Cancer,148, 146 - 158.
Leo, P., Chandramouli, S., Farre, X., Elliott, R., Janowczyk, A., Bera, K., Fu, P., Janaki, N., El-Fahmawi, A., Shahait, M., Kim, J., Lee, D., Yamoah, K., Rebbeck, T., Khani, F., Robinson, B., Shih, N., Feldman, M., Gupta, S., McKenney, J., Lai, P., & Madabhushi, A.(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.
Eck, B., Chirra, P., Muchhala, A., Hall, S., Bera, K., Tiwari, P., Madabhushi, A., Seiberlich, N., & Viswanath, S. E.(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).
Koyuncu, C., Lu, C., Bera, K., Zhang, Z., Xue, Z., Toro, P., Corredor-Prada, G., Chute, D., Fu, P., Thorstad, W., Faraji, F., Bishop, J., Mehrad, M., Castro, P., Sikora, A., Thompson, L., Chernock, R., Lang Kuhs, K., Luo, J., Sandulache, V., Adelstein, D., Koyfman, S., Lewis, Jr, J., & Madabhushi, A.(2021).Computerized tumor multinucleation index (MuNI) is prognostic in p16+ oropharyngeal carcinoma.The Journal of Clinical Investigation,131(8),e145488.
Atta-Fosu, T., LaBarbara, M., Ghose, S., Schoenhagen, P., Saliba, W., Tchou, P., Lindsay, B., Desai, M., Kwon, D., Chung, M., & Madabhushi, A.(2021).A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT.BMC Medical Imaging,21(1),45.
Freeman, B., Maji, D., Nrasimhan, S., Ahuja, S., Little, J., Suster, M., Mohseni, P., & Gurkan, U.(2021).Microfluidic electrical impedance assessment of red blood cell-mediated microvascular occlusion..Lab on a chip.
Firouznia, M., Feeney, A., LaBarbera, M., McHale, M., Cantlay, C., Kalfas, N., Schoenhagen, P., Saliba, W., Tchou, P., Barnard, J., Chung, M., & Madabhushi, A.(2021).Machine Learning-Derived Fractal Features of Shape and Texture of the Left Atrium and Pulmonary Veins From Cardiac Computed Tomography Scans Are Associated With Risk of Recurrence of Atrial Fibrillation Postablation.Circulation: Arrhythmia and Electrophysiology,14(3),e009265.
Chen, Y., Zee, J., Smith, A., Jayapandian, C., Hodgin, J., Howell, D., Palmer, M., Thomas, D., Cassol, C., Farris, A., Perkinson, K., Madabhushi, A., Barisoni, L., & Janowczyk, A.(2021).Assessment of a computerized quantitative quality control tool for whole slide images of kidney biopsies.Journal of Pathology,253(3),268-278.
Zhou, K., Greenspan, H., Davatzikos, C., Duncan, J., Van Ginneken, B., Madabhushi, A., Prince, J., Rueckert, D., & Summers, R.(2021).A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises.Proceedings of the IEEE,109(5),820 - 838.
Wolff, F., Weyer, D., Papachristou, C. A., & Clay, S. A.(2021).Design for Reliability: Tradeoffs between Lifetime and Performance due to Electromigration.Elsevier, Microelectronics Reliability,117, 12 pages.
Lu, C., Koyuncu, C., Corredor-Prada, G., Prasanna, P., Leo, P., WAng, X., Janowczyk, A., Bera, K., Lewis, J., Velcheti, V., & Madabhushi, A.(2021).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
