Spring 2022

CIG is looking for motivated WashU students for the following projects. We have a history of mentoring undergraduate and graduate students interested in research, with many of our alumni becoming PhD students in top programs (including WashU) and many projects leading to publications in top venues (see below). If you are interested in a given project, please apply by emailing your resume and a short description of your interests to the project supervisor.

Detecting multiple myeloma using deep image analysis

Status: reserved (Junhao Hu)
Description: Multiple myeloma (MM) is a devastating form of cancer. The development of imaging tools for MM is an active area with the potential to enable breakthroughs in its diagnosis and treatment. CIG is looking for a motivated student to develop new deep learning methods in the context of our MM collaboration with the researchers at the School of Medicine.
Supervision: Flora Sun (zhixin.sun@wustl.edu), Ulugbek Kamilov
Areas: Image Analysis, Deep Learning

Deep learning for diagnosis of post-traumatic elbow disease

Status: reserved (Hugh James)
Description: Deep learning may revolutionize the study of musculoskeletal conditions by automating the evaluation pipeline. This project seeks to develop tissue and modality-specific DL algorithms for automatic evaluation of disease stage and explore multi-modal in post-traumatic elbow conditions using histology, computed tomography, and magnetic resonance imaging. This project is in collaboration with the Musculoskeletal Soft Tissue Laboratory.
Supervision: Weijie Gan (weijie.gan@wustl.edu), Michael David, Spencer Lake, Ulugbek Kamilov
Areas: Image Analysis, Deep Learning

Deep learning for the next-generation microscopy

Status: reserved (Xiangyu Chen)
Description: Lightsheet microscopy has had an impact on fields as diverse as developmental and cell biology, anatomical science, biophysics and neuroscience. The goal of this collaborative project is to develop a new image restoration, processing, and analysis pipeline for light-sheet microscopy based on deep learning.
Supervision: Jiaming Liu (jiaming.liu@wustl.edu), Ulugbek Kamilov
Areas: Computational Microscopy, Deep Learning

Learning strategies for MRI reconstruction

Status: reserved (Harry Gao)
Description: Magnetic resonance imaging can be accelerated by collecting less data in the k-space. This project seeks to develop deep learning strategies for enabling high-quality image reconstruction under limited data. CIG seeks a motivated student interested in deep learning and medical imaging for this project.
Supervision: Weijie Gan (weijie.gan@wustl.edu), Ulugbek Kamilov
Areas: Medical Imaging, Deep Learning

Memory and computationally efficient deep learning

Status: reserved (Zihao Zou)
Description: Deep learning (DL) has led to a paradigm shift in computational imaging. However, training of large DL models is emerging as a major challenge for the technology. In this project, we explore new strategies for enabling memory and computationally efficient DL in the context of computational imaging.
Supervision: Jiaming Liu (jiaming.liu@wustl.edu), Ulugbek Kamilov
Areas: Deep Learning, Large-Scale Optimization

Memory-efficient deep image restoration

Status: reserved (Julia Zeng)
Description: In many medical applications, acquired images are corrupted with undesirable imaging artifacts and noise. Deep learning has recently emerged as a powerful strategy for computationally restoring corrupted images. In this project, the goal is to explore memory-efficient deep learning strategies for medical image restoration.
Supervision: Xiaojian Xu (xiaojianxu@wustl.edu), Ulugbek Kamilov
Areas: Deep Learning, Medical Imaging

Inhomogeneity correction in quantitative brain MRI

Status: reserved (Eddie Chandler)
Description: Magnetic field inhomogeneities adversely affect MRI images. The correction of such inhomogeneities can be formulated as an optimization problem that exploits their mathematical model and machine learning (ML). CIG is seeking a motivated student interested in using optimization and ML for medical imaging.
Supervision: Xiaojian Xu (xiaojianxu@wustl.edu), Ulugbek Kamilov
Areas: Image Analysis, Deep Learning

Deep learning for calf muscle and fat measurement

Status: reserved (Yixuan Luo)
Description: This project seeks to use DIXON MRI images to measure subcutaneous fat, bone, muscle, inter-/intramuscular fat, and individual muscle compartments. WashU School of Medicine conducts studies where overweight individuals need their calf muscle automatically measured. Muscle atrophy is a typical consequence of weight loss which is associated with health risks in the older population. CIG is seeking a motivated student interested in using deep learning methods to automatically segment the calf muscle/fat.
Supervision: Xiaojian Xu (xiaojianxu@wustl.edu), Paul Commean, Ulugbek Kamilov
Areas: Image Analysis, Deep Learning

Fall 2021

  • Regularization by artifact removal for PET (J. Choi, W. Gan, and U. S. Kamilov)
  • Deep learning for diagnosis of post-traumatic elbow disease (Y. Yuan, W. Gan, M. David, S. Lake, and U. S. Kamilov)
  • Implementing optimization algorithms in Jupyter Notebook (X. Huang and U. S. Kamilov)
  • Deep learning for abdominal visceral and subcutaneous fat measurement (T. Lee, X. Xu, P. Commean, and U. S. Kamilov)
  • Deep learning for calf muscle and fat measurement (Y. Luo, X. Xu, P. Commean, and U. S. Kamilov)
  • Synthesizing high-resolution videos using deep learning (W. Shangguan, Y. Sun, and U. S. Kamilov)
  • Asynchronous plug-and-play priors with theoretical guarantees (H. Qin, Y. Sun, and U. S. Kamilov)
  • Learning Optimal Sampling Strategies for MRI (D. Jin, W. Gan, and U. S. Kamilov)
  • Image Transformers for Computational Imaging (Z. Zhang, J. Liu, and U. S. Kamilov)
  • Detecting multiple myeloma using deep image analysis (F. Sun, W. Gan, M. Shokeen, and U. S. Kamilov)
  • Inhomogeneity correction in quantitative brain MRI (E. Chandler, X. Xu, and U. S. Kamilov)

Spring 2021

  • Next generation robust learning algorithms (R. Liu, Y. Sun, and U. S. Kamilov)
  • Accurate calibration of coil sensitivities in MRI (Y. Hu, W. Gan, and U. S. Kamilov)
  • Synthesizing high-resolution videos using deep learning (W. Shangguan, Y. Sun, and U. S. Kamilov)
  • Tunable deep learning for computational imaging (A. Zhou, X. Xu, and U. S. Kamilov)
  • Low-complexity learning for large-scale imaging (Z. Wang, J. Liu, and U. S. Kamilov)
  • Deep learning for diagnosis of post-traumatic elbow disease (Y. Yuan, W. Gan, M. David, S. Lake, and U. S. Kamilov)
  • Deep learning for quantitative brain imaging (E. Chandler, X. Xu, and U. S. Kamilov)

Fall 2020

  • Deep Learning for quantitative brain imaging (E. Chandler, X. Xu, Y. Sun, and U. S. Kamilov)
  • Cycle GAN for quantitative MRI (Y. Li, X. Xu, and U. S. Kamilov)
  • Training robust convolutional neural networks (R. Liu, Y. Sun, and U. S. Kamilov)
  • Implementing CNNs with Tensorflow 2.0 (Z. Wang, Y. Sun, and U. S. Kamilov)
  • Autocalibration for regularization by denoising (M. Xie, Y. Sun, and U. S. Kamilov)

Spring 2020

  • Machine learning for 4D MRI (W. Gan, H. An, U. S. Kamilov)
  • Proximal Optimization for Computerized Tomography (R. Schurz, J. Liu, U. S. Kamilov)
  • End-to-End Training of VAE-GAN Priors (G. Meng, J. Liu, U. S. Kamilov)
  • Design of GAN Prios for Iterative Algorithms (A. Bani, J. Liu, U. S. Kamilov)
  • Recovery Experiments for Deep Learning (J. Liu, Y. Sun, U. S. Kamilov)
  • Autocalibration for Regularization by Denoising (M. Xie, Y. Sun, and U. S. Kamilov)
  • Stochastic Algorithms using Deep Priors (Z. Wu, Y. Sun, U. S. Kamilov)
  • Fast Automatic Focal Plane Selection for Brightfield Microscopy using Machine Learning (Y. Song, X. Xu, N. Laird, Z. Pincus, U. S. Kamilov)
  • Nonlocal Learning for Image Restoration (Y. Li, X. Xu, U. S. Kamilov)
  • Motion-artifact Correction in Quantitative MRI (H. Tang, X. Xu, U. S. Kamilov)

Fall 2019

  • Motion artifact reduction in quantitative brain MRI (N. Woythaler, X. Xu, U. S. Kamilov)
  • Large-Scale Optimization for Computational Imaging (Y. Jia, U. S. Kamilov)
  • Generative Adversarial Networks for Image Restoration (R. Suzuki, X. Xu, U. S. Kamilov)
  • Adversarially robust classifiers for image reconstruction (E. Tang, X. Xu, U. S. Kamilov)
  • Stochastic Algorithms using Deep Priors (Z. Wu, Y. Sun, U. S. Kamilov)
  • Asynchronous large-scale optimization for imaging (Y. Sun, Y. Sun, U. S. Kamilov)
  • Next generation Cryo-EM imaging (M. Xie, Y. Sun, U. S. Kamilov)
  • Computational methods for quantitative brain MR imaging (M. Torop, Y. Sun, U. S. Kamilov)

Spring 2019

  • Deep Single-Molecule Super-Resolution Imaging (S. Xu, Y. Sun, H. Mazidi, M. Lew, U. S. Kamilov)
  • Learning for material imaging with light (Z. Wu, Y. Sun, U. S. Kamilov)
  • Regularization with ConvNets (J. Liu, Y. Sun, U. S. Kamilov)

Fall 2018

  • Regularization with ConvNets (J. Liu, Y. Sun, U. S. Kamilov)
  • Learning for 3D computational imaging (W. Gan, X. Xu, U. S. Kamilov)
  • Convolutional dictionary learning for image reconstruction (F. Long, X. Xu, U. S. Kamilov)
  • Unsupervised deep image learning (H. Zou, Y. Sun, U. S. Kamilov)
  • Distributed algorithms for nonconvex stochastic optimization (M. Abijaoude, U. S. Kamilov)
  • Image restoration with GANs (F. Zhai, Y. Sun, U. S. Kamilov)
  • Learning for MRI (J. Xing, X. Xu, U. S. Kamilov)
  • Learning for material imaging with light (Z. Wu, Y. Sun, U. S. Kamilov)
  • Dictionary Learning for Fourier Ptychographic Microscopy (S. Xu, X. Xu, U. S. Kamilov)