| A review of machine learning in obesity KW DeGregory, P Kuiper, T DeSilvio, JD Pleuss, R Miller, JW Roginski, ... Obesity Reviews 19 (5), 668-685, 2018 | 222 | 2018 |
| Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 tesla endorectal, in vivo T2‐weighted MR imagery SE Viswanath, NB Bloch, JC Chappelow, R Toth, NM Rofsky, EM Genega, ... Journal of Magnetic Resonance Imaging 36 (1), 213-224, 2012 | 169 | 2012 |
| Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: preliminary findings A Algohary, S Viswanath, R Shiradkar, S Ghose, S Pahwa, D Moses, ... Journal of Magnetic Resonance Imaging 48 (3), 818-828, 2018 | 131 | 2018 |
| Radiomics Analysis on FLT-PET/MRI for Characterization of Early Treatment Response in Renal Cell Carcinoma: A Proof-of-Concept Study J Antunes, S Viswanath, M Rusu, L Valls, C Hoimes, N Avril, ... Translational Oncology 9 (2), 155-162, 2016 | 122 | 2016 |
| Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI R Shiradkar, TK Podder, A Algohary, S Viswanath, RJ Ellis, ... Radiation oncology 11, 1-14, 2016 | 100 | 2016 |
| Multimodal wavelet embedding representation for data combination (MaWERiC): integrating magnetic resonance imaging and spectroscopy for prostate cancer detection P Tiwari, S Viswanath, J Kurhanewicz, A Sridhar, A Madabhushi NMR in Biomedicine 25 (4), 607-619, 2012 | 97 | 2012 |
| Computer assisted diagnosis (CAD) of cancer using multi-functional, multi-modal in-vivo magnetic resonance spectroscopy (MRS) and imaging (MRI) MD Feldman, S Viswanath, P Tiwari, R Toth, A Madabhushi, ... US Patent 8,295,575, 2012 | 78 | 2012 |
| Radiomic Features of Primary Rectal Cancers on Baseline T2‐Weighted MRI Are Associated With Pathologic Complete Response to Neoadjuvant Chemoradiation … JT Antunes, A Ofshteyn, K Bera, EY Wang, JT Brady, JE Willis, ... Journal of Magnetic Resonance Imaging 52 (5), 1531-1541, 2020 | 69 | 2020 |
| A Comprehensive Segmentation, Registration, and Cancer Detection Scheme on 3 Tesla In Vivo Prostate DCE-MRI S Viswanath, BN Bloch, E Genega, N Rofsky, R Lenkinski, J Chappelow, ... Medical Image Computing and Computer-Assisted Intervention–MICCAI 2008: 11th …, 2008 | 68 | 2008 |
| Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings G Penzias, A Singanamalli, R Elliott, J Gollamudi, N Shih, M Feldman, ... PloS one 13 (8), e0200730, 2018 | 66 | 2018 |
| Integrating structural and functional imaging for computer assisted detection of prostate cancer on multi-protocol in vivo 3 Tesla MRI S Viswanath, BN Bloch, M Rosen, J Chappelow, R Toth, N Rofsky, ... Medical Imaging 2009: Computer-Aided Diagnosis 7260, 1000-1011, 2009 | 65 | 2009 |
| MRQy—An open‐source tool for quality control of MR imaging data AR Sadri, A Janowczyk, R Zhou, R Verma, N Beig, J Antunes, ... Medical Physics 47 (12), 6029-6038, 2020 | 58 | 2020 |
| Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results C Alvarez-Jimenez, AA Sandino, P Prasanna, A Gupta, SE Viswanath, ... Cancers 12 (12), 3663, 2020 | 53 | 2020 |
| Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study SE Viswanath, PV Chirra, MC Yim, NM Rofsky, AS Purysko, MA Rosen, ... BMC Medical Imaging 19 (1), 22, 2019 | 53 | 2019 |
| Novel PCA‐VIP scheme for ranking MRI protocols and identifying computer‐extracted MRI measurements associated with central gland and peripheral zone prostate tumors SB Ginsburg, SE Viswanath, BN Bloch, NM Rofsky, EM Genega, ... Journal of Magnetic Resonance Imaging 41 (5), 1383-1393, 2015 | 50 | 2015 |
| Radiomics and radiogenomics: technical basis and clinical applications R Li, L Xing, S Napel, DL Rubin CRC Press, 2019 | 48 | 2019 |
| Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI P Chirra, P Leo, M Yim, BN Bloch, AR Rastinehad, A Purysko, M Rosen, ... Journal of Medical Imaging 6 (2), 024502-024502, 2019 | 43 | 2019 |
| Interplay between bias field correction, intensity standardization, and noise filtering for T2-weighted MRI D Palumbo, B Yee, P O'Dea, S Leedy, S Viswanath, A Madabhushi 2011 Annual International Conference of the IEEE Engineering in Medicine and …, 2011 | 42 | 2011 |
| Enhanced multi-protocol analysis via intelligent supervised embedding (EMPrAvISE): detecting prostate cancer on multi-parametric MRI S Viswanath, BN Bloch, J Chappelow, P Patel, N Rofsky, R Lenkinski, ... Medical Imaging 2011: Computer-Aided Diagnosis 7963, 239-253, 2011 | 38 | 2011 |
| Co-Registration of ex vivo Surgical Histopathology and in vivo T2 weighted MRI of the Prostate via multi-scale spectral embedding representation L Li, S Pahwa, G Penzias, M Rusu, J Gollamudi, S Viswanath, ... Scientific Reports 7 (1), 8717, 2017 | 35 | 2017 |
Anant MadabhushiDepartment of Biomedical Engineering, Professor, Biomedical Engineering, Emory Univ & Georgia TechVerified email at emory.edu
