Quantitative cellular imaging
My recent completed projects and in review manuscripts of using deep learning and marchine learning in biomedical imaging with Dr. Kwonmoo Lee (now at Boston Children’s Hospital):
C. Wang, H. Choi, L. Woodbury, K. Lee, Deep Learning-based Subcellular Phenotyping of Protrusion Dynamics Reveals Fine Differential Drug Responses at Subcellular and Single-Cell Levels, bioRxiv, 2022.
K. Vaidyanathan*, C. Wang*, Y. Yu, A. Krajnik, M. Choi, B. Lin, J. Kolega, K. Lee#, Y. Bae#, Machine learning approach reveals heterogeneous responses to FAK and Rho GTPases inhibition on smooth muscle spheroid formation, (In review) bioRxiv 927616, 2020.
H. Choi, C. Wang, X. Pan, M. Cao, J. Brazzo , Y. Bae, K. Lee, Emerging machine learning approaches to phenotyping temporally heterogeneous cellular processes, In review, 2020.
C. Wang*, H. J. Choi*, S. Kim, A. Desai, N. Lee, D. Kim, Y. Bae, K. Lee, Deconvolution of subcellular protrusion heterogeneity and the underlying actin regulator dynamics from live cell imaging, Nature Communications, 9, 1688, 2018.
S. Kim*, C. Wang*, B. Zhao, H. Im, J. Min, N. Choi, C. M. Castro, R. Weissleder, H. Lee#, K. Lee#. Deep transfer learning-based hologram classification for molecular diagnostics. Scientific Reports, 8:17003, 2018.
C. Wang, X. Zhang, Y. Chen, K. Lee. vU-net: Accurate cell edge segmentation in time-lapse fluorescence live cell images based on convolutional neural network, bioRxiv 191858, 2017
C. Wang, S. Kang, E. Kim, X. Zhang, H. J. Choi, A. Choi, K. Lee, Edge detection of cryptic lamellipodia assisted by deep learning, bioRxiv 181263, 2017