Vertebra segmentation from biplanar whole-spine radiographs is highly demanded in the quantitative assessment of scoliosis and resultant sagittal deformities. https://www.selleckchem.com/products/mrtx1257.html However, vertebra segmentation is challenging due to the low contrast, blended boundaries, and superimposition of many layers, especially in the sagittal plane. To alleviate these problems, we propose a lightweight pyramid attention quick refinement network (LPAQR-Net) for efficient and accurate vertebra segmentation from biplanar whole-spine radiographs.
The LPAQR-Net consists of three components (1) a lightweight backbone network (LB-Net) to prune network parameters and memory footprints, (2) a series of global attention refinement (GAR) to selectively reuse low-level features to facilitate the feature refinement, and (3) an attention-based atrous spatial pyramid pooling (A-ASPP) to extract weighted pyramid contexts to improve the segmentation of blurred vertebrae. A multi-class training strategy is employed to alleviate the over-segmentation of ar accurate vertebra localization to improve the segmentation of blurred vertebrae. Significant The method provides efficient and accurate vertebra segmentation from frontal and lateral whole-spine radiographs in which can help clinicians with a fast and reproducible evaluation of spinal deformity.This paper proposes a two-way multi-ringed forest (TMR-Forest) to estimating the malignancy of the pulmonary nodules for false positive reduction (FPR). Based on our previous work of deep decision framework, named MR-Forest, we generate a growing path mode on predefined pseudo-timeline of L time slots to build pseudo-spatiotemporal features. It synchronously works with FPR based on MR-Forest to help predict the labels from a dynamic perspective. Concretely, Mask R-CNN is first used to recommend the bounding boxes of ROIs and classify their pathological features. Afterward, hierarchical attribute matching is introduced to obtain the input ROIs' attribute layouts and select the candidates for their growing path generation. The selected ROIs can replace the fixed-sized ROIs' fitting results at different time slots for data augmentation. A two-stage counterfactual path elimination is used to screen out the input paths of the cascade forest. Finally, a simple label selection strategy is executed to output the predicted label to point out the input nodule's malignancy. On 1034 scans of the merged dataset, the framework can report more accurate malignancy labels to achieve a better CPM score of 0.912, which exceeds those of MR-Forest and 3DDCNNs about 2.8% and 4.7%, respectively.Learning the gene coexpression pattern is a central challenge for high-dimensional gene expression analysis. Recently, sparse singular value decomposition (SVD) has been used to achieve this goal. However, this model ignores the structural information between variables (e.g., a gene network). The typical graph-regularized penalty can be used to incorporate such prior graph information to achieve more accurate discovery and better interpretability. However, the existing approach fails to consider the opposite effect of variables with negative correlations. In this article, we propose a novel sparse graph-regularized SVD model with absolute operator (AGSVD) for high-dimensional gene expression pattern discovery. The key of AGSVD is to impose a novel graph-regularized penalty (|u|TL|u|). However, such a penalty is a nonconvex and nonsmooth function, so it brings new challenges to model solving. We show that the nonconvex problem can be efficiently handled in a convex fashion by adopting an alternating optimization strategy. The simulation results on synthetic data show that our method is more effective than the existing SVD-based ones. In addition, the results on several real gene expression data sets show that the proposed methods can discover more biologically interpretable expression patterns by incorporating the prior gene network.Deep convolutional neural networks (CNNs) have demonstrated promising performance on image classification tasks, but the manual design process becomes more and more complex due to the fast depth growth and the increasingly complex topologies of CNNs. As a result, neural architecture search (NAS) has emerged to automatically design CNNs that outperform handcrafted counterparts. However, the computational cost is immense, e.g., 22,400 GPU-days and 2000 GPU-days for two outstanding NAS works named NAS and NASNet, respectively, which motivates this work. A new effective and efficient surrogate-assisted particle swarm optimization (PSO) algorithm is proposed to automatically evolve CNNs. This is achieved by proposing a novel surrogate model, a new method of creating a surrogate data set, and a new encoding strategy to encode variable-length blocks of CNNs, all of which are integrated into a PSO algorithm to form the proposed method. The proposed method shows its effectiveness by achieving the competitive error rates of 3.49% on the CIFAR-10 data set, 18.49% on the CIFAR-100 data set, and 1.82% on the SVHN data set. The CNN blocks are efficiently learned by the proposed method from CIFAR-10 within 3 GPU-days due to the acceleration achieved by the surrogate model and the surrogate data set to avoid the training of 80.1% of CNN blocks represented by the particles. Without any further search, the evolved blocks from CIFAR-10 can be successfully transferred to CIFAR-100, SVHN, and ImageNet, which exhibits the transferability of the block learned by the proposed method.This article first investigates the issue on dynamic learning from adaptive neural network (NN) control of discrete-time strict-feedback nonlinear systems. To verify the exponential convergence of estimated NN weights, an extended stability result is presented for a class of discrete-time linear time-varying systems with time delays. Subsequently, by combining the n-step-ahead predictor technology and backstepping, an adaptive NN controller is constructed, which integrates the novel weight updating laws with time delays and without the σ modification. After ensuring the convergence of system output to a recurrent reference signal, the radial basis function (RBF) NN is verified to satisfy the partial persistent excitation condition. By the combination of the extended stability result, the estimated NN weights can be verified to exponentially converge to their ideal values. The convergent weight sequences are comprehensively represented and stored by constructing some elegant learning rules with some novel sequences and the mod function. The stored knowledge is used again to develop a neural learning control scheme. Compared with the traditional adaptive NN control, the proposed scheme can not only accomplish the same or similar tracking tasks but also greatly improve the transient control performance and alleviate the online computation. Finally, the validity of the presented scheme is illustrated by numerical and practical examples.In this article, we study the consensus problem in the framework of networked multiagent systems with constraint where there exists antagonistic information. A major difficulty is how to characterize the communication among the interacting agents in the presence of antagonistic information without resorting to the signed graph theory, which plays a central role in the Altafini model. It is shown that the proposed control protocol enables us to solve the consensus problem in a node-based viewpoint where both cooperative and antagonistic interactions coexist. Moreover, the proposed setup is further extended to the case of input saturation, leading to the semiglobal consensus. In addition, the consensus region associated with antagonistic information among participating individuals is also elaborated. Finally, the deduced theoretical results are applied to the task distribution problem via unmanned ground vehicles.Dimensionality reduction (DR) technique has been frequently used to alleviate information redundancy and reduce computational complexity. Traditional DR methods generally are inability to deal with nonlinear data and have high computational complexity. To cope with the problems, we propose a fast unsupervised projection (FUP) method. The simplified graph of FUP is constructed by samples and representative points, where the number of the representative points selected through iterative optimization is less than that of samples. By generating the presented graph, it is proved that large-scale data can be projected faster in numerous scenarios. Thereafter, the orthogonality FUP (OFUP) method is proposed to ensure the orthogonality of projection matrix. Specifically, the OFUP method is proved to be equivalent to PCA upon certain parameter setting. Experimental results on benchmark data sets show the effectiveness in retaining the essential information.Many data sources, such as human poses, lie on low-dimensional manifolds that are smooth and bounded. Learning low-dimensional representations for such data is an important problem. One typical solution is to utilize encoder-decoder networks. However, due to the lack of effective regularization in latent space, the learned representations usually do not preserve the essential data relations. For example, adjacent video frames in a sequence may be encoded into very different zones across the latent space with holes in between. This is problematic for many tasks such as denoising because slightly perturbed data have the risk of being encoded into very different latent variables, leaving output unpredictable. To resolve this problem, we first propose a neighborhood geometric structure-preserving variational autoencoder (SP-VAE), which not only maximizes the evidence lower bound but also encourages latent variables to preserve their structures as in ambient space. Then, we learn a set of small surfaces to approximately bound the learned manifold to deal with holes in latent space. We extensively validate the properties of our approach by reconstruction, denoising, and random image generation experiments on a number of data sources, including synthetic Swiss roll, human pose sequences, and facial expression images. The experimental results show that our approach learns more smooth manifolds than the baselines. We also apply our approach to the tasks of human pose refinement and facial expression image interpolation where it gets better results than the baselines.Accurate electroencephalogram (EEG) pattern decoding for specific mental tasks is one of the key steps for the development of brain-computer interface (BCI), which is quite challenging due to the considerably low signal-to-noise ratio of EEG collected at the brain scalp. Machine learning provides a promising technique to optimize EEG patterns toward better decoding accuracy. However, existing algorithms do not effectively explore the underlying data structure capturing the true EEG sample distribution and, hence, can only yield a suboptimal decoding accuracy. To uncover the intrinsic distribution structure of EEG data, we propose a clustering-based multitask feature learning algorithm for improved EEG pattern decoding. Specifically, we perform affinity propagation-based clustering to explore the subclasses (i.e., clusters) in each of the original classes and then assign each subclass a unique label based on a one-versus-all encoding strategy. With the encoded label matrix, we devise a novel multitask learning algorithm by exploiting the subclass relationship to jointly optimize the EEG pattern features from the uncovered subclasses.