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Many of today's 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.
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. Computerized tissue histopathology image analysis and computer-aided diagnosis is very efficient and has become amenable. The cell detection process is the most basic step among the computer-aided histopathology image analysis applications. In this chapter, we study a deep convolutional neural network-based method for the lung cancer cell detection problem. This problem is very challenging due to many reasons, e.g., cell clumping and overlapping, high complexity of the cell detection methods, and the lack of humanly annotated datasets. To address these issues, we introduce a deep learning-based cell detection method for the effectiveness, as the deep learning methods have been demonstrated to be repeatedly successful in various computer vision applications in the last decade. However, this method still takes very long time to detect cells in very small images, e.g., 512x512, albeit it is very effective in the cell detection task. In order to reduce the overall time cost of this method, we combine this method with the sparse kernel technique to significantly accelerate the cell detection process, up to 500 times. With the aforementioned advances, our numerical results confirm that the resulting method is able to outperform most state-of-the-art cell detection methods in terms of both efficiency and effectiveness.
Structured sparse learning has become a popular and mature research field. Among all structured sparse models, we found an interesting fact that most structured sparse properties could be captured by convolution operators, most famous ones being total variation and wavelet sparsity. This finding has naturally brought us to a generalization termed as Convolutional Sparsity. While this generalization bridges the convolution and sparse learning theory, we are able to propose a general, efficient, hyperparameter-free optimization algorithm framework for convolutional sparse models, thanks to the analysis theory of convolution operators. The convergence of the general, hyperparameter-free algorithm has been comprehensively analyzed, with a non-ergodic rate of $\mathcal{O}(1/\varepsilon^2)$ and ergodic rate of $\mathcal{O}(1/\varepsilon)$, where $\varepsilon$ is the desired accuracy. Extensive experiments confirm the superior performance of our general algorithm in various convolutional sparse models, even better than some application-specialistic algorithms.
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 millions or billions of 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 hundred-million-pixel image in 20 s (approximately 10, 000 cells per second) on a single workstation, which is an encouraging result in whole-slide imaging practice.
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.
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.
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 $\mathcal{O}(1/N)$ for $N$ iterations, which is much faster than $\mathcal{O}(1/\sqrt{N})$ 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.
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 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.
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.
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.