ConMLP: MLP-Based Self-Supervised Contrastive Learning for Skeleton Data Analysis and Action Recognition.

Human action recognition has drawn significant attention because of its importance in computer vision-based applications. Action recognition based on skeleton sequences has rapidly advanced in the last decade. Conventional deep learning-based approaches are based on extracting skeleton sequences through convolutional operations. Most of these architectures are implemented by learning spatial and temporal features through multiple streams. These studies have enlightened the action recognition endeavor from various algorithmic angles. However, three common issues are observed: (1) The models are usually complicated; therefore, they have a correspondingly higher computational complexity. (2) For supervised learning models, the reliance on labels during training is always a drawback. (3) Implementing large models is not beneficial to real-time applications. To address the above issues, in this paper, we propose a multi-layer perceptron (MLP)-based self-supervised learning framework with a contrastive learning loss function (ConMLP). ConMLP does not require a massive computational setup; it can effectively reduce the consumption of computational resources. Compared with supervised learning frameworks, ConMLP is friendly to the huge amount of unlabeled training data. In addition, it has low requirements for system configuration and is more conducive to being embedded in real-world applications. Extensive experiments show that ConMLP achieves the top one inference result of 96.9% on the NTU RGB+D dataset. This accuracy is higher than the state-of-the-art self-supervised learning method. Meanwhile, ConMLP is also evaluated in a supervised learning manner, which has achieved comparable performance to the state of the art of recognition accuracy.

A Two-stage Diagnostic Framework for Post-ablation Treatment Response Assessment in Patients with Hepatocellular Carcinoma.

Contrast-enhanced ultrasound (CEUS) video plays an important role in post-ablation treatment response assessment in patients with hepatocellular carcinoma (HCC). However, the assessment of treatment response using CEUS video is challenging due to issues such as high inter-frame data repeatability, small ablation area and poor imaging quality of CEUS video. To address these issues, we propose a two-stage diagnostic framework for post-ablation treatment response assessment in patients with HCC using CEUS video. The first stage is a location stage, which is used to locate the ablation area. At this stage, we propose a Yolov5-SFT to improve the location results of the ablation area and a similarity comparison module (SCM) to reduce data repeatability. The second stage is an assessment stage, which is used for the evaluation of postoperative efficacy. At this stage, we design an EfficientNet-SK to improve assessment accuracy. The Experimental results on the self-collected data show that the proposed framework outperforms other selected algorithms, and can effectively assist doctors in the assessment of post-ablation treatment response.

IFT-Net: Interactive Fusion Transformer Network for Quantitative Analysis of Pediatric Echocardiography.

The task of automatic segmentation and measurement of key anatomical structures in echocardiography is critical for subsequent extraction of clinical parameters. However, the influence of boundary blur, speckle noise, and other factors increase the difficulty of fully automatically segmenting 2D ultrasound images. The previous research has addressed this challenge using convolutional neural networks (CNN), which fails to consider global contextual information and long-range dependency. To further improve the quantitative analysis of pediatric echocardiography, this paper proposes an interactive fusion transformer network (IFT-Net) for quantitative analysis of pediatric echocardiography, which achieves the bidirectional fusion between local features and global context information by constructing interactive learning between the convolution branch and the transformer branch. First, we construct a dual-attention pyramid transformer (DPT) branch to model the long-range dependency from spatial and channels and enhance the learning of global context information. Second, we design a bidirectional interactive fusion (BIF) unit that fuses the local and global features interactively, maximizes their preservation and refines the segmentation. Finally, we measure the clinical anatomical parameters through key point positioning. Based on the parasternal short-axis (PSAX) view of the heart base from pediatric echocardiography, we segment and quantify the right ventricular outflow tract (RVOT) and aorta (AO) with promising results, indicating the potential clinical application. The code is publicly available at: https://github.com/Zhaocheng1/IFT-Net.

Masculinity, Injury, and Death: Implications for Anti-Knife-Carrying Messages.

Although knives are the most common homicide instrument in Britain, factors that influence knife-carrying tolerance (i.e., the extent to which it is seen as acceptable and justified) and perceptions of anti-knife messages (i.e., slogans and posters aimed at reducing knife crime) have not been examined, which the current article will cover by featuring progressively related studies. In Study 1, 227 men took part in a study on factors associated with knife-carrying. In Study 2, 200 participants took part in an experimental study on anti-knife slogans. In Study 3, 169 men took part in a study on existing anti-knife injury posters. In Study 4, 151 men took part in a study on anti-knife CGI posters featuring an avatar with different types of knife injury. Study 1 proposes a structural equation model that shows the intercorrelations between physical defense ability, limited trust in authority, limited control over one's status and the need for respect, and how they predict aggressive masculinity (i.e., macho culture), which, in turn, predicts knife-carrying tolerance. The model also reveals two significant latent factors: saving face inter-male competition (i.e., honor) and perceived social ecological constraints (i.e., socioeconomic limitations). Study 2 shows that the injury slogan was rated as most persuasive. Study 3 shows that the fresh injury poster was rated as most persuasive, emotional, and believable. Study 4 shows that it was the eye injury that was rated as most persuasive, emotional, and believable. The article supports protection motivation theory and offers practical insights into tackling knife crime.

Placental Super Micro-vessels Segmentation Based on ResNeXt with Convolutional Block Attention and U-Net.

Accurate placenta super micro-vessels segmentation is the key to diagnose placental diseases. However, the current automatic segmentation algorithm has issues of information redundancy and low information utilization, which reduces the segmentation accuracy. To solve this problem, we propose a model based on ResNeXt with convolutional block attention module (CBAM) and UNet (RC-UNet) for placental super micro-vessels segmentation. In the RC-UNet model, we choose the UNet as the backbone network for initial feature extraction. At the same time, we select ResNeXt-CBAM as the attention module for feature refinement and weighting. Specifically, we stack the blocks of the same topology following the split-transform-merge strategy to reduce the redundancy of hyperparameter. Moreover, we conduct CBAM processing on each group of the detailed features to get informative features and suppress unnecessary features, which improve the information utilization. The experiments on the self-collected data show that the proposed algorithm has better segmentation results for anatomical structures (umbilical cord blood (UC), stem villus (ST), maternal blood (MA)) than other selected algorithms.

Lattice-Boltzmann interactive blood flow simulation pipeline.

PURPOSE: Cerebral aneurysms are one of the prevalent cerebrovascular disorders in adults worldwide and caused by a weakness in the brain artery. The most impressive treatment for a brain aneurysm is interventional radiology treatment, which is extremely dependent on the skill level of the radiologist. Hence, accurate detection and effective therapy for cerebral aneurysms still remain important clinical challenges. In this work, we have introduced a pipeline for cerebral blood flow simulation and real-time visualization incorporating all aspects from medical image acquisition to real-time visualization and steering. METHODS: We have developed and employed an improved version of HemeLB as the main computational core of the pipeline. HemeLB is a massive parallel lattice-Boltzmann fluid solver optimized for sparse and complex geometries. The visualization component of this pipeline is based on the ray marching method implemented on CUDA capable GPU cores. RESULTS: The proposed visualization engine is evaluated comprehensively and the reported results demonstrate that it achieves significantly higher scalability and sites updates per second, indicating higher update rate of geometry sites' values, in comparison with the original HemeLB. This proposed engine is more than two times faster and capable of 3D visualization of the results by processing more than 30 frames per second. CONCLUSION: A reliable modeling and visualizing environment for measuring and displaying blood flow patterns in vivo, which can provide insight into the hemodynamic characteristics of cerebral aneurysms, is presented in this work. This pipeline increases the speed of visualization and maximizes the performance of the processing units to do the tasks by breaking them into smaller tasks and working with GPU to render the images. Hence, the proposed pipeline can be applied as part of clinical routines to provide the clinicians with the real-time cerebral blood flow-related information.

Effect of crosslinking in cartilage-like collagen microstructures.

The mechanical performance of biological tissues is underpinned by a complex and finely balanced structure. Central to this is collagen, the most abundant protein in our bodies, which plays a dominant role in the functioning of tissues, and also in disease. Based on the collagen meshwork of articular cartilage, we have developed a bottom-up spring-node model of collagen and examined the effect of fibril connectivity, implemented by crosslinking, on mechanical behaviour. Although changing individual crosslink stiffness within an order of magnitude had no significant effect on modelling predictions, the density of crosslinks in a meshwork had a substantial impact on its behaviour. Highly crosslinked meshworks maintained a 'normal' configuration under loading, with stronger resistance to deformation and improved recovery relative to sparsely crosslinked meshwork. Stress on individual fibrils, however, was higher in highly crosslinked meshworks. Meshworks with low numbers of crosslinks reconfigured to disease-like states upon deformation and recovery. The importance of collagen interconnectivity may provide insight into the role of ultrastructure and its mechanics in the initiation, and early stages, of diseases such as osteoarthritis.

A constituent-based preprocessing approach for characterising cartilage using NIR absorbance measurements.

Near-infrared spectroscopy is a widely adopted technique for characterising biological tissues. The high dimensionality of spectral data, however, presents a major challenge for analysis. Here, we present a second-derivative Beer's law-based technique aimed at projecting spectral data onto a lower dimension feature space characterised by the constituents of the target tissue type. This is intended as a preprocessing step to provide a physically-based, low dimensionality input to predictive models. Testing the proposed technique on an experimental set of 145 bovine cartilage samples before and after enzymatic degradation, produced a clear visual separation between the normal and degraded groups. Reduced proteoglycan and collagen concentrations, and increased water concentrations were predicted by simple linear fitting following degradation (all [Formula: see text]). Classification accuracy using the Mahalanobis distance was [Formula: see text] between these groups.

Using a passive coordinate measurement arm for motion tracking of a rigid endoscope for augmented-reality image-guided surgery.

BACKGROUND: One of the main sources of error in commercial surgical navigation systems is the tracking of surgical tools. Mainstream systems typically use optical or electromagnetic tracking technologies, which exhibit accuracies of the order of 1 mm. The objective of this study was to introduce a lightweight high-precision passive coordinate measurement arm into an augmented reality-based surgical navigation system to track a rigid endoscope. METHODS: A series of dry laboratory experiments were run to compare the tracking performance of an optical tracking device, a passive coordinate measurement arm and a hybrid set-up. RESULTS: The optical device displayed overlay errors in the range 1.5-3 mm. For the precision measurement arm, 96% of overlay errors were < 1 mm. The hybrid set-up exhibited overlay errors in the range 0.8-1.5 mm. CONCLUSIONS: The reported experiments showed that a high-precision articulated measurement arm could be used as a motion-tracking device for surgical instruments in augmented-reality surgical navigation.