Zheng Xu

Accelerated Dynamic MRI Reconstruction with Total Variation and Nuclear Norm Regularization

In this paper, we propose a novel compressive sensing model for dynamic MR reconstruction. With total variation (TV) and nuclear norm (NN) regularization, our method can utilize both spatial and temporal redundancy in dynamic MR images. Due to the non-smoothness and non-separability of TV and NN terms, it is difficult to optimize the primal problem. To address this issue, we propose a fast algorithm by solving a primal-dual form of the original problem. The ergodic convergence rate of the proposed method is \(\mathcal{O}(1/N)\) for N iterations. In comparison with six state-of-the-art methods, extensive experiments on single-coil and multi-coil dynamic MR data demonstrate the superior performance of the proposed method in terms of both reconstruction accuracy and time complexity.

Subtype Cell Detection with an Accelerated Deep Convolution Neural Network

Robust cell detection in histopathological images is a crucial step in the computer-assisted diagnosis methods. In addition, recent studies show that subtypes play an significant role in better characterization of tumor growth and outcome prediction. In this paper, we propose a novel subtype cell detection method with an accelerated deep convolution neural network. The proposed method not only detects cells but also gives subtype cell classification for the detected cells. Based on the subtype cell detection results, we extract subtype cell related features and use them in survival prediction. We demonstrate that our proposed method has excellent subtype cell detection performance and our proposed subtype cell features can achieve more accurate survival prediction.

Detecting 10,000 cells in one second

In this paper, we present a generalized distributed deep neural network architecture to detect cells in whole-slide high-resolution histopathological images, which usually hold \(10^{8}\) to \(10^{10}\) pixels. Our framework can adapt and accelerate any deep convolutional neural network pixel-wise cell detector to perform whole-slide cell detection within a reasonable time limit. We accelerate the convolutional neural network forwarding through a sparse kernel technique, eliminating almost all of the redundant computation among connected patches. Since the disk I/O becomes a bottleneck when the image size scale grows larger, we propose an asynchronous prefetching technique to diminish a large portion of the disk I/O time. An unbalanced distributed sampling strategy is proposed to enhance the scalability and communication efficiency in distributed computing. Blending advantages of the sparse kernel, asynchronous prefetching and distributed sampling techniques, our framework is able to accelerate the conventional convolutional deep learning method by nearly 10, 000 times with same accuracy. Specifically, our method detects cells in a \(10^{8}\)-pixel (\(10^4\times 10^4\)) image in 20 s (approximately 10, 000 cells per second) on a single workstation, which is an encouraging result in whole-slide imaging practice.

Efficient Preconditioning in Joint Total Variation Regularized Parallel MRI Reconstruction

Parallel magnetic resonance imaging (pMRI) is a useful technique to aid clinical diagnosis. In this paper, we develop an accelerated algorithm for joint total variation (JTV) regularized calibrationless Parallel MR image reconstruction. The algorithm minimizes a linear combination of least squares data fitting term and the joint total variation regularization. This model has been demonstrated as a very powerful tool for parallel MRI reconstruction. The proposed algorithm is based on the iteratively reweighted least squares (IRLS) framework, which converges exponentially fast. It is further accelerated by preconditioned conjugate gradient method with a well-designed preconditioner. Numerous experiments demonstrate the superior performance of the proposed algorithm for parallel MRI reconstruction in terms of both accuracy and efficiency.

Efficient Lung Cancer Cell Detection with Deep Convolution Neural Network

Lung cancer cell detection serves as an important step in the automation of cell-based lung cancer diagnosis. In this paper, we propose a robust and efficient lung cancer cell detection method based on the accelerated Deep Convolution Neural Network framework(DCNN). The efficiency of the proposed method is demonstrated in two aspects: (1) We adopt a training strategy, learning the DCNN model parameters from only weakly annotated cell information (one click near the nuclei location). This technique significantly reduces the manual annotation cost and the training time. (2) We introduce a novel DCNN forward acceleration technique into our method, which speeds up the cell detection process several hundred times than the conventional sliding-window based DCNN. In the reported experiments, state-of-the-art accuracy and the impressive efficiency are demonstrated in the lung cancer histopathological image dataset.

An effective approach for robust lung cancer cell detection

As lung cancer is one of the most frequent and serious disease causing death for both men and women, early diagnosis and differentiation of lung cancers is clinically important. Lung cancer cell detection is the most basic step among the Computer-aided histopathology lung image analysis applications. We proposed an automatic lung cancer cell detection method based on deep convolutional neural network. In this method, we need only the weakly annotated images to achieve the image patches as the training set. The detection problem is formulated into a deep learning framework using these patches efficiently. Then, the feature extraction is made through the training of the deep convolutional neural networks. A challenging clinical use case including hundreds of patients’ lung cancer histopathological images is used in our experiment. Our method has achieved promising performance on the lung cancer cell detection in terms of accuracy and efficiency.

Seq2seq Fingerprint: An Unsupervised Deep Molecular Embedding for Drug Discovery

Many of todays drug discoveries require expertise knowledge and insanely expensive biological experiments for identifying the chemical molecular properties. However, despite the growing interests of using supervised machine learning algorithms to automatically identify those chemical molecular properties, there is little advancement of the performance and accuracy due to the limited amount of training data. In this paper, we propose a novel unsupervised molecular embedding method, providing a continuous feature vector for each molecule to perform further tasks, e.g., solubility classification. In the proposed method, a multi-layered Gated Recurrent Unit (GRU) network is used to map the input molecule into a continuous feature vector of fixed dimensionality, and then another deep GRU network is employed to decode the continuous vector back to the original molecule. As a result, the continuous encoding vector is expected to contain rigorous and enough information to recover the original molecule and predict its chemical properties. The proposed embedding method could utilize almost unlimited molecule data for the training phase. With sufficient information encoded in the vector, the proposed method is also robust and task-insensitive. The performance and robustness are confirmed and interpreted in our extensive experiments.

RSPIRIT: Robust self-consistent parallel imaging reconstruction based on generalized Lasso

In this paper, we propose a novel approach called robust iterative self-consistent parallel imaging reconstruction (RSPIRiT) in parallel magnetic resonance imaging (pMRI). Different from the smooth Tikhonov regularization used in SPIRiT, our model utilizes generalized Lasso to fix calibration errors in the reconstruction process. It results in a non-smooth optimization problem, which we introduce a new primal-dual pMRI algorithm to solve. We conduct extensive experiments to demonstrate the effectiveness of our approach, compared to state-of-the-art methods.

Group-Driven Reinforcement Learning for Personalized mHealth Intervention

Due to the popularity of smartphones and wearable devices nowadays, mobile health (mHealth) technologies are promising to bring positive and wide impacts on peoples health. State-of-the-art decision-making methods for mHealth rely on some ideal assumptions. Those methods either assume that the users are completely homogenous or completely heterogeneous. However, in reality, a user might be similar with some, but not all, users. In this paper, we propose a novel group-driven reinforcement learning method for the mHealth. We aim to understand how to share information among similar users to better convert the limited user information into sharper learned RL policies. Specifically, we employ the K-means clustering method to group users based on their trajectory information similarity and learn a shared RL policy for each group. Extensive experiment results have shown that our method can achieve clear gains over the state-of-the-art RL methods for mHealth.

Accelerated sparse optimization for missing data completion

In this paper, we propose an algorithm for missing value recovery of visual data such as image or video. These missing values may result from the corruption in acquisition process, or user-specified unexpected outliers. This problem exists in wide range of applications. We use the nuclear norm (NN) regularization to enforce the global consistency of the image, while the total variation (TV) regularization is used to encourage the locally consistent in image intensity domain. This model can be applied in very challenging scenarios, where only very small amount of data is available. However, it is very difficult to efficiently solve these two regularizations simultaneously by convex programming due to its composite structure and non-smoothness. To this end, we propose an efficient proximal-splitting algorithm for joint NN/TV minimization. The proposed algorithm is theoretically guaranteed to achieve a convergence rate of O(1/N) for N iterations, which is much faster than O(1/√N) by the black-box first-order method for solving the non-smooth optimization problem. In our experiments, we demonstrate the superior performance of our algorithm on image completion compared with seven state-of-the-art algorithms.