Sevgi, D., Srivastava, S., Whitney, J., O’Connell, M., Sil Kar, S., Hu, M., ... Ehlers, J.(2021).Characterization of Ultra-widefield Angiographic Vascular Features in Diabetic 2 Retinopathy with Automated Severity Classification. Ophthalmology Science,(). DOI: 10.1016/j.xops.2021.100049
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,(). DOI: 10.1002/cjp2.229
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., ... 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. DOI: 10.1016/S2589-7500(21)00082-0
Schömig-Markiefka , B., Pryalukhin, A., Hulla, W., Bychkov, A., Fukuoka, J., Madabhushi, A., ... Tolkach, Y.(2021).Quality control stress test for deep learning-based diagnostic model in digital pathology. Modern Pathology,(). DOI: 10.1038/s41379-021-00859-x
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., ... 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. DOI: 10.3390/cancers13112781
Leo, P., Janowczyk, A., Elliott, R., Janaki, N., Bera, K., Shiradkar, R., ... 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. DOI: 10.1038/s41698-021-00174-3
Khorrami, M., Bera, K., Thawani, R., Rajiah, P., Gupta, A., Fu, P., ... 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. DOI: 10.1016/j.ejca.2021.02.008
Leo, P., Chandramouli, S., Farre, X., Elliott, R., Janowczyk, A., Bera, K., ... 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,(). DOI: 10.1016/j.euf.2021.04.016
Eck, B., Chirra, P., Muchhala, A., Hall, S., Bera, K., Tiwari, P., ... 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),(). DOI: 10.1002/jmri.27635
Atta-Fosu, T., LaBarbara, M., Ghose, S., Schoenhagen, P., Saliba, W., Tchou, P., ... 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. DOI:
Firouznia, M., Feeney, A., LaBarbera, M., McHale, M., Cantlay, C., Kalfas, N., ... 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.
Zhou, K., Greenspan, H., Davatzikos, C., Duncan, J., Van Ginneken, B., Madabhushi, A., ... 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. DOI: 10.1109/JPROC.2021.3054390
Prasanna, P., Bobba, V., Figueiredo, N., Sevgi, D., Lu, C., Braman, N., ... Others(2021).Radiomics-based assessment of ultra-widefield leakage patterns and vessel network architecture in the PERMEATE study: insights into treatment durability. British Journal of Ophthalmology, 105(8), 1155--1160.
