| Prior to initiation of chemotherapy, can we predict breast tumor response? Deep learning convolutional neural networks approach using a breast MRI tumor dataset R Ha, C Chin, J Karcich, MZ Liu, P Chang, S Mutasa, E Pascual Van Sant, ... Journal of digital imaging 32, 693-701, 2019 | 126 | 2019 |
| Predicting breast cancer molecular subtype with MRI dataset utilizing convolutional neural network algorithm R Ha, S Mutasa, J Karcich, N Gupta, E Pascual Van Sant, J Nemer, M Sun, ... Journal of digital imaging 32, 276-282, 2019 | 107 | 2019 |
| Convolutional neural networks for the detection and measurement of cerebral aneurysms on magnetic resonance angiography JN Stember, P Chang, DM Stember, M Liu, J Grinband, CG Filippi, ... Journal of digital imaging 32, 808-815, 2019 | 89 | 2019 |
| The role of initial chest X-ray in triaging patients with suspected COVID-19 during the pandemic HW Kim, KM Capaccione, G Li, L Luk, RS Widemon, O Rahman, ... Emergency radiology 27, 617-621, 2020 | 77 | 2020 |
| Axillary lymph node evaluation utilizing convolutional neural networks using MRI dataset R Ha, P Chang, J Karcich, S Mutasa, R Fardanesh, RT Wynn, MZ Liu, ... Journal of digital imaging 31, 851-856, 2018 | 70 | 2018 |
| Convolutional neural network using a breast MRI tumor dataset can predict oncotype Dx recurrence score R Ha, P Chang, S Mutasa, J Karcich, S Goodman, E Blum, K Kalinsky, ... Journal of Magnetic Resonance Imaging 49 (2), 518-524, 2019 | 60 | 2019 |
| Convolutional neural network based breast cancer risk stratification using a mammographic dataset R Ha, P Chang, J Karcich, S Mutasa, EP Van Sant, MZ Liu, ... Academic radiology 26 (4), 544-549, 2019 | 50 | 2019 |
| Convolutional neural network detection of axillary lymph node metastasis using standard clinical breast MRI T Ren, R Cattell, H Duanmu, P Huang, H Li, R Vanguri, MZ Liu, ... Clinical breast cancer 20 (3), e301-e308, 2020 | 47 | 2020 |
| Fully automated convolutional neural network method for quantification of breast MRI fibroglandular tissue and background parenchymal enhancement R Ha, P Chang, E Mema, S Mutasa, J Karcich, RT Wynn, MZ Liu, ... Journal of digital imaging 32, 141-147, 2019 | 42 | 2019 |
| Evaluation of neonatal brain myelination using the T1‐and T2‐weighted MRI ratio JE Soun, MZ Liu, KA Cauley, J Grinband Journal of Magnetic Resonance Imaging 46 (3), 690-696, 2017 | 40 | 2017 |
| Predicting post neoadjuvant axillary response using a novel convolutional neural network algorithm R Ha, P Chang, J Karcich, S Mutasa, EP Van Sant, E Connolly, C Chin, ... Annals of surgical oncology 25, 3037-3043, 2018 | 36 | 2018 |
| Trends and diagnostic value of D-dimer levels in patients hospitalized with coronavirus disease 2019 C Creel-Bulos, M Liu, SC Auld, M Gaddh, CL Kempton, M Sharifpour, ... Medicine 99 (46), e23186, 2020 | 33 | 2020 |
| A novel CNN algorithm for pathological complete response prediction using an I-SPY TRIAL breast MRI database MZ Liu, S Mutasa, P Chang, M Siddique, S Jambawalikar, R Ha Magnetic resonance imaging 73, 148-151, 2020 | 32 | 2020 |
| Can diffusion‐weighted imaging serve as a biomarker of fibrosis in pancreatic adenocarcinoma? EM Hecht, MZ Liu, MR Prince, S Jambawalikar, HE Remotti, SW Weisberg, ... Journal of Magnetic Resonance Imaging 46 (2), 393-402, 2017 | 32 | 2017 |
| Multi-site, multi-platform comparison of MRI T1 measurement using the system phantom KE Keenan, Z Gimbutas, A Dienstfrey, KF Stupic, MA Boss, SE Russek, ... PLoS One 16 (6), e0252966, 2021 | 27 | 2021 |
| Repeatability of quantitative diffusion-weighted imaging metrics in phantoms, head-and-neck and thyroid cancers: preliminary findings R Paudyal, AS Konar, NA Obuchowski, V Hatzoglou, TL Chenevert, ... Tomography 5 (1), 15-25, 2019 | 27 | 2019 |
| Weakly supervised deep learning approach to breast MRI assessment MZ Liu, C Swintelski, S Sun, M Siddique, E Desperito, S Jambawalikar, ... Academic Radiology 29, S166-S172, 2022 | 24 | 2022 |
| Accuracy of distinguishing atypical ductal hyperplasia from ductal carcinoma in situ with convolutional neural network–based machine learning approach using mammographic image data R Ha, S Mutasa, EPV Sant, J Karcich, C Chin, MZ Liu, S Jambawalikar American Journal of Roentgenology 212 (5), 1166-1171, 2019 | 23 | 2019 |
| Low temperature copper-nanorod bonding for 3D integration PI Wang, T Karabacak, J Yu, HF Li, GG Pethuraja, SH Lee, MZ Liu, JQ Lu, ... MRS Online Proceedings Library (OPL) 970, 0970-Y04-07, 2006 | 23 | 2006 |
| Deep learning prediction of axillary lymph node status using ultrasound images S Sun, S Mutasa, MZ Liu, J Nemer, M Sun, M Siddique, E Desperito, ... Computers in Biology and Medicine 143, 105250, 2022 | 22 | 2022 |
Sachin JambawalikarColumbia University Medical CenterVerified email at columbia.edu
