Stevensonmay3710

To train the nonlinear mapping, a staged representation transfer (SRT) strategy is introduced to maximumly leverage the semantic and discriminative information from the training data. We trained the model with LUS data of 233 patients, and validated it with 80 patients. Our method can effectively combine the two modalities and achieve accuracy of 75.0% for 4-level patient severity assessment, and 87.5% for the binary severe/non-severe identification. Besides, our method also provides interpretation of the severity assessment by grading each of the lung zone (with accuracy of 85.28%) and identifying the pathological patterns of each lung zone. Our method has a great potential in real clinical practice for COVID-19 patients, especially for pregnant women and children, in aspects of progress monitoring, prognosis stratification, and patient management.Limb salvage surgery of malignant pelvic tumors is the most challenging procedure in musculoskeletal oncology due to the complex anatomy of the pelvic bones and soft tissues. It is crucial to accurately resect the pelvic tumors with appropriate margins in this procedure. selleck kinase inhibitor However, there is still a lack of efficient and repetitive image planning methods for tumor identification and segmentation in many hospitals. In this paper, we present a novel deep learning-based method to accurately segment pelvic bone tumors in MRI. Our method uses a multi-view fusion network to extract pseudo-3D information from two scans in different directions and improves the feature representation by learning a relational context. In this way, it can fully utilize spatial information in thick MRI scans and reduce over-fitting when learning from a small dataset. Our proposed method was evaluated on two independent datasets collected from 90 and 15 patients, respectively. The segmentation accuracy of our method was superior to several comparing methods and comparable to the expert annotation, while the average time consumed decreased about 100 times from 1820.3 seconds to 19.2 seconds. In addition, we incorporate our method into an efficient workflow to improve the surgical planning process. Our workflow took only 15 minutes to complete surgical planning in a phantom study, which is a dramatic acceleration compared with the 2-day time span in a traditional workflow.Deep learning models (with neural networks) have been widely used in challenging tasks such as computer-aided disease diagnosis based on medical images. Recent studies have shown deep diagnostic models may not be robust in the inference process and may pose severe security concerns in clinical practice. Among all the factors that make the model not robust, the most serious one is adversarial examples. The so-called "adversarial example" is a well-designed perturbation that is not easily perceived by humans but results in a false output of deep diagnostic models with high confidence. In this paper, we evaluate the robustness of deep diagnostic models by adversarial attack. Specifically, we have performed two types of adversarial attacks to three deep diagnostic models in both single-label and multi-label classification tasks, and found that these models are not reliable when attacked by adversarial example. We have further explored how adversarial examples attack the models, by analyzing their quantitative classification results, intermediate features, discriminability of features and correlation of estimated labels for both original/clean images and those adversarial ones. We have also designed two new defense methods to handle adversarial examples in deep diagnostic models, i.e., Multi-Perturbations Adversarial Training (MPAdvT) and Misclassification-Aware Adversarial Training (MAAdvT). The experimental results have shown that the use of defense methods can significantly improve the robustness of deep diagnostic models against adversarial attacks.Ultrasound is a widely used imaging modality, yet it is well-known that scanning can be highly operator-dependent and difficult to perform, which limits its wider use in clinical practice. The literature on understanding what makes clinical sonography hard to learn and how sonography varies in the field is sparse, restricted to small-scale studies on the effectiveness of ultrasound training schemes, the role of ultrasound simulation in training, and the effect of introducing scanning guidelines and standards on diagnostic image quality. The Big Data era, and the recent and rapid emergence of machine learning as a more mainstream large-scale data analysis technique, presents a fresh opportunity to study sonography in the field at scale for the first time. Large-scale analysis of video recordings of full-length routine fetal ultrasound scans offers the potential to characterise differences between the scanning proficiency of experts and trainees that would be tedious and time-consuming to do manually due to theion of constituent tasks.Toxoplasma gondii (T. gondii) is a zoonotic apicomplexan protozoan that can cause reproductive losses in ruminants across the world. Therefore, a meta-analysis was conducted to estimate the worldwide prevalence of T. gondii infection in the aborted fetuses and stillbirths of sheep, goat, and cattle. Moreover, it attempted to evaluate the prevalence rate of T. gondii infection in ruminants that had abortions using serological methods. Based on the keywords, a systematic search of six databases was conducted to retrieve cross-sectional articles in English-language. Data were synthesized to calculate the overall prevalence of T. gondii infection worldwide using the random-effects model with a 95 % confidence interval (CI). Moreover, the present study includes sensitivity analysis, publication bias test, and quality assessment of the studies. The final analyses included 37, 19, and 8 studies conducted on sheep (4383 aborted fetuses and stillbirths as well as 1940 abortive sheep), goat (248 aborted fetuses and sti and goats as well as reduce the economic damage to the livestock industry.

Decreased mechanical work done by the trailing limb when descending a single-step could affect load development and increase injury risk on the leading limb. This study assessed the effect of trailing limb mechanics on the development of lead limb load during a step descent by examining individuals with unilateral transtibial amputations who are known to exhibit reduced work in the prosthetic limb.

Eight amputees and 10 able-bodied controls walked 5m along the length of a raised platform, descended a single-step of 14cm height, and continued walking. The intact limb of amputees led during descent. Kinematic and kinetic data were recorded using integrated motion capture and force platform system. Lead limb loading was assessed through vertical ground reaction force, and knee moments and joint reaction forces. Sagittal-plane joint work was calculated for the ankle, knee, and hip in both limbs.

No differences were found in lead limb loading despite differences in trail limb mechanics evidenced by amputees performing 58% less total work by the trailing (prosthetic) limb to lower the centre of mass (P=0.