Staffordbryant0720

Comparing with the state-of-the-art methods, MANs and C-MANs improve the performance significantly and achieve the best results on six data sets for action recognition. The source code has been made publicly available at https//github.com/memory-attention-networks.Technological advancements in high-throughput genomics enable the generation of complex and large data sets that can be used for classification, clustering, and bio-marker identification. Modern deep learning algorithms provide us with the opportunity of finding most significant features in such huge dataset to characterize diseases (e.g., cancer) and their sub-types. Thus, developing such deep learning method, which can successfully extract meaningful features from various breast cancer sub-types, is of current research interest. In this paper, we develop dual stage (unsupervised pre-training and supervised fine-tuning) neural network architecture termed AFExNet based on adversarial auto-encoder (AAE) to extract features from high dimensional genetic data. We evaluated the performance of our model through twelve different supervised classifiers to verify the usefulness of the new features using public RNA-Seq dataset of breast cancer. AFExNet provides consistent results in all performance metrics across twelve different classifiers which makes our model classifier independent. We also develop a method named "TopGene" to find highly weighted genes from the latent space which could be useful for finding cancer bio-markers. Put together, AFExNet has great potential for biological data to accurately and effectively extract features. Our work is fully reproducible and source code can be downloaded from Github https//github.com/NeuroSyd/breast-cancer-sub-types.High frame rate (HFR) echo-particle image velocimetry (echoPIV) is a promising tool for measuring intracardiac blood flow dynamics. In this study we investigate the optimal ultrasound contrast agent (UCA SonoVue®) infusion rate and acoustic output to use for HFR echoPIV (PRF = 4900 Hz) in the left ventricle (LV) of patients. Three infusion rates (0.3, 0.6 and 1.2 ml/min) and five acoustic output amplitudes (by varying transmit voltage 5V, 10V, 15V, 20V and 30V - corresponding to Mechanical Indices of 0.01, 0.02, 0.03, 0.04 and 0.06 at 60 mm depth) were tested in 20 patients admitted for symptoms of heart failure. We assess the accuracy of HFR echoPIV against pulsed wave Doppler acquisitions obtained for mitral inflow and aortic outflow. In terms of image quality, the 1.2 ml/min infusion rate provided the highest contrast-to-background (CBR) ratio (3 dB improvement over 0.3 ml/min). The highest acoustic output tested resulted in the lowest CBR. Increased acoustic output also resulted in increased microbubble disruption. For the echoPIV results, the 1.2 ml/min infusion rate provided the best vector quality and accuracy; and mid-range acoustic outputs (corresponding to 15V-20V transmit voltages) provided the best agreement with the pulsed wave Doppler. Overall, the highest infusion rate (1.2 ml/min) and mid-range acoustic output amplitudes provided the best image quality and echoPIV results.We introduce a generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The model assumes that the images in the dataset are non-linear mappings of low-dimensional latent vectors. We use the deep convolutional neural network (CNN) to represent the non-linear transformation. The parameters of the generator as well as the low-dimensional latent vectors are jointly estimated only from the undersampled measurements. This approach is different from traditional CNN approaches that require extensive fully sampled training data. We penalize the norm of the gradients of the non-linear mapping to constrain the manifold to be smooth, while temporal gradients of the latent vectors are penalized to obtain a smoothly varying time-series. The proposed scheme brings in the spatial regularization provided by the convolutional network. The main benefit of the proposed scheme is the improvement in image quality and the orders-of-magnitude reduction in memory demand compared to traditional manifold models. To minimize the computational complexity of the algorithm, we introduce an efficient progressive training-in-time approach and an approximate cost function. These approaches speed up the image reconstructions and offers better reconstruction performance.Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning. Based on deep neural networks, previous studies have shown promising technologies for brain glioma segmentation. PF 429242 inhibitor However, these approaches lack powerful strategies to incorporate contextual information of tumor cells and their surrounding, which has been proven as a fundamental cue to deal with local ambiguity. In this work, we propose a novel approach named Context-Aware Network (CANet) for brain glioma segmentation. CANet captures high dimensional and discriminative features with contexts from both the convolutional space and feature interaction graphs. We further propose context guided attentive conditional random fields which can selectively aggregate features. We evaluate our method using publicly accessible brain glioma segmentation datasets BRATS2017, BRATS2018 and BRATS2019. The experimental results show that the proposed algorithm has better or competitive performance against several State-of-The-Art approaches under different segmentation metrics on the training and validation sets.Rheumatology workforces are increasingly challenged by too few physicians in face of the growing burden of rheumatic and musculoskeletal diseases (RMDs). Rheumatology is one of the most frequent non-surgical specialty referrals and has the longest wait times for subspecialists. We used a population-based approach to describe changes in the rheumatology workforce, patient volumes and geographic variation in the supply of and access to rheumatologists, in Ontario, Canada, between 2000 and 2019, and projected changes in supply by 2030. Over time, we observed greater feminization of the workforce and increasing age of workforce members. We identified a large regional variation in rheumatology supply. Fewer new patients are seen annually, which likely contributes to increasing wait times and reduced access to care. Strategies and policies to raise the critical mass and improve regional distribution of supply to effectively provide rheumatology care and support the healthcare delivery of patients with RMDs are needed.