profile

Mingjie Gao

I am a Ph.D. candidate in Electrical and Computer Engineering in the Department of Electrical Engineering and Computer Science at the University of Michigan, advised by Prof. Jeffrey A. Fessler and Prof. Heang-Ping Chan. I am working on improving the image quality and cancer diagnosis of digital breast tomosynthesis (DBT) through model-based reconstruction, denoising and signal enhancement.

     

Research Interests

My major study area is signal & image processing and machine learning. My research interests include medical imaging, image reconstruction, inverse problems, optimization, and deep learning.

Journal Publications

  • Deep Convolutional Neural Network With Adversarial Training for Denoising Digital Breast Tomosynthesis Images

    M. Gao, J. A. Fessler, and H.-P. Chan, IEEE Transactions on Medical Imaging, Vol. 40, No. 7, pp. 1805-1816, July 2021. Paper Supplement Code

Conference Proceedings and Abstracts

  • Deep Convolutional Neural Network Regularized Digital Breast Tomosynthesis Reconstruction With Detector Blur and Correlated Noise Modeling

    M. Gao, J. A. Fessler, and H.-P. Chan, in Proceedings of SPIE, 12031, 1203108, 2022. Paper Slides

  • Plug-and-play Reconstruction With Deep Learning Denoising for Improving Detectability of Microcalcifications in Digital Breast Tomosynthesis Images

    M. Gao, J. A. Fessler, and H.-P. Chan, in RSNA Scientific Assembly and Annual Meeting, 2021. Slides

  • Digital Breast Tomosynthesis Denoising Using Deep Convolutional Neural Network: Effects of Dose Level of Training Target Images

    M. Gao, J. A. Fessler, and H.-P. Chan, in Proceedings of SPIE, 11595, 115951K, 2021. Paper Slides

  • Training Deep Convolutional Neural Network With In Silico Data for Denoising Digital Breast Tomosynthesis Images

    M. Gao, J. A. Fessler, and H.-P. Chan, in RSNA Scientific Assembly and Annual Meeting, 2020. Slides

  • Deep Convolutional Neural Network Denoising for Digital Breast Tomosynthesis Reconstruction

    M. Gao, R. K. Samala, J. A. Fessler, and H.-P. Chan, in Proceedings of SPIE, 11312, 113120Q, 2020. Paper Slides

  • Myelin Water Fraction Estimation Using Small-tip Fast Recovery MRI

    S. T. Whitaker, G. Nataraj, M. Gao, J.-F. Nielsen, and J. A. Fessler, in Proceedings of ISMRM Annual Conference, 2019. Paper

  • Kernel Regression for Fast Myelin Water Imaging

    G. Nataraj, M. Gao, J.-F. Nielsen, and J. A. Fessler, in ISMRM Workshop on Machine Learning Part II, 2018 (2nd-place poster award).

  • Shallow Learning With Kernels for Dictionary-free Magnetic Resonance Fingerprinting

    G. Nataraj, M. Gao, J. Assländer, C. Scott, and J. A. Fessler, in ISMRM Workshop on MR Fingerprinting, 2017. Paper Slides


Contact Information
gmingjie@umich.edu
Med Inn Building
1500 E Medical Center Dr Rm C470
Ann Arbor MI 48109
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Page updated May 8, 2022