First-Principles Prediction of High and Low Resistance States in Ta/h-BN/Ta Atomristor.

Two-dimensional (2D) materials have received significant attention for their potential use in next-generation electronics, particularly in nonvolatile memory and neuromorphic computing. This is due to their simple metal-insulator-metal (MIM) sandwiched structure, excellent switching performance, high-density capability, and low power consumption. In this work, using comprehensive material simulations and device modeling, the thinnest monolayer hexagonal boron nitride (h-BN) atomristor is studied by using a MIM configuration with Ta electrodes. Our first-principles calculations predicted both a high resistance state (HRS) and a low resistance state (LRS) in this device. We observed that the presence of van der Waals (vdW) gaps between the Ta electrodes and monolayer h-BN with a boron vacancy (VB) contributes to the HRS. The combination of metal electrode contact and the adsorption of Ta atoms onto a single VB defect (TaB) can alter the interface barrier between the electrode and dielectric layer, as well as create band gap states within the band gap of monolayer h-BN. These band gap states can shorten the effective tunneling path for electron transport from the left electrode to the right electrode, resulting in an increase in the current transmission coefficient of the LRS. This resistive switching mechanism in monolayer h-BN atomristors can serve as a theoretical reference for device design and optimization, making them promising for the development of atomristor technology with ultra-high integration density and ultra-low power consumption.

Automatic Assessment of Ultrasound Curvature Angle for Scoliosis Detection Using 3-D Ultrasound Volume Projection Imaging.

OBJECTIVE: Scoliosis is a spinal deformation in which the spine takes a lateral curvature, generating an angle in the coronal plane. The conventional method for detecting scoliosis is measurement of the Cobb angle in spine images obtained by anterior X-ray scanning. Ultrasound imaging of the spine is found to be less ionising than traditional radiographic modalities. For posterior ultrasound scanning, alternate indices of the spinous process angle (SPA) and ultrasound curve angle (UCA) were developed and have proven comparable to those of the traditional Cobb angle. In SPA, the measurements are made using the spinous processes as an anatomical reference, leading to an underestimation of the traditionally used Cobb angles. Alternatively, in UCA, more lateral features of the spine are employed for measurement of the main thoracic and thoracolumbar angles; however, clear identification of bony features is required. The current practice of UCA angle measurement is manual. This research attempts to automate the process so that the errors related to human intervention can be avoided and the scalability of ultrasound scoliosis diagnosis can be improved. The key objective is to develop an automatic scoliosis diagnosis system using 3-D ultrasound imaging. METHODS: The novel diagnosis system is a three-step process: (i) finding the ultrasound spine image with the most visible lateral features using the convolutional RankNet algorithm; (ii) segmenting the bony features from the noisy ultrasound images using joint spine segmentation and noise removal; and (iii) calculating the UCA automatically using a newly developed centroid pairing and inscribed rectangle slope method. RESULTS: The proposed method was evaluated on 109 patients with scoliosis of different severity. The results obtained had a good correlation with manually measured UCAs (R2=0.9784 for the main thoracic angle andR2=0.9671 for the thoracolumbar angle) and a clinically acceptable mean absolute difference of the main thoracic angle (2.82 ± 2.67°) and thoracolumbar angle (3.34 ± 2.83°). CONCLUSION: The proposed method establishes a very promising approach for enabling the applications of economic 3-D ultrasound volume projection imaging for mass screening of scoliosis.

[Value of contrast-enhanced percutaneous ultrasound in the diagnosis of rotator cuff tear subtype].

OBJECTIVE: To explore characteristics of contrast-enhanced ultrasound (CEUS) images features and diagnostic value of rotator cuff tear subtypes. METHODS: From January 2019 to March 2022, percutaneous ultrasound-guided subacromial bursography (PUSB) with persutaneous ultrasound-guide tendon lesionography (PUTL) was performed on 114 patients with suspected rotator cuff injury were evaluated, including 54 males and 60 females ranged in age from 35 to 75 years old with an average of (58.8±8.7 ) years old;76 patients on the right side and 38 patients on the left side;the course of disease ranged from 0.13 to 111 months with an average of (10.2±9.8) months. GE LOGIQ E9 color doppler ultrasound diagnostic high frequency(6 to 12 MHz) was used to CEUS Using arthroscopy as gold standard, receiver operating characteristic (ROC) curve was used to evaluate diagnostic efficacy of US, MRI and CEUS for rotator cuff injury, also sensitivity, specificity, positive predictive value, negative predictive value and accuracy were calculated. RESULTS: The sensitivity of US in diagnosing full-thickness tears was 72.1%, specificity was 93.0%, and accuracy was 85.1%. The sensitivity, specificity and accuracy of MRI diagnosis of full-thickness tear were 90.9%, 92.6% and 92.1% respectively. The sensitivity, specificity and accuracy of CEUS in diagnosis of full-thickness tear were 100%. The sensitivity, specificity and accuracy of US in the diagnosis of partial tear were 85.7%, 77.2% and 79.8% respectively. The sensitivity, specificity and accuracy of MRI diagnosis of partial tear were 83.7%, 81.7% and 82.5% respectively. The sensitivity, specificity and accuracy of CEUS in diagnosis of partial tear were 95.7%, 92.6% and 93.9% respectively. There were significant differences in diagnosis results of US, MRI and CEUS for rotator cuff bursa tear (P<0.001). Kapp test showed good consistency between CEUS and arthroscopy in diagnosing rotator cuff tear subtypes (full-thickness and partial tears). CONCLUSION: Using PUSB/PUTL to observe distribution of contrast media in bursa, tendon and joint cavity to evaluate the type of rotator cuff tear, its diagnostic performance is significantly better than US and MRI. Therefore, percutaneous contrast-enhanced ultrasound can be a reliable method for diagnosing subtypes of rotator cuff tears.

The Influence of Social Capital: A Trigger for Increasing Job Satisfaction.

Purpose: Evidence suggests that social capital in medical and health institutions is associated with the job satisfaction of medical staff. We examined the relationship between the social capital of Primary Healthcare Institutions (PHI) and the job satisfaction of pharmacists within it. Materials and Methods: From August 24 to September 1, 2021, we visited a total of 253 PHIs in 31 provinces of China. The social capital of healthcare organizations reported by employees (SOCAPO-E) scale was used to measure the social capital level of PHIs. And the Minnesota short-form job satisfaction scale was used to obtain pharmacists' job satisfaction. We employed multiple linear regression to explore the relationship between the social capital of PHI and pharmacists' job satisfaction. We also examined the effects of pharmacists' individual characteristics and job-related factors on pharmacists' job satisfaction. Results: It was statistically significant that the higher the social capital stock of PHI, the higher the job satisfaction level of pharmacists becomes. In addition, the regression analysis revealed that work hours, employment form, license acquired condition, disputes with patients and training frequency were significantly associated with the job satisfaction of pharmacists in PHI. Conclusion: Social capital in PHI has a significant impact on pharmacists' job satisfaction, suggesting that investing in social capital in PHI is a valuable investment in China. Furthermore, trust, which can be divided into affective trust and cognitive trust, and reciprocity are vital to the fulfillment of pharmacists' job satisfaction as core elements of social capital.

Simulation of a Steep-Slope p- and n-Type HfS2/MoTe2 Field-Effect Transistor with the Hybrid Transport Mechanism.

The use of a two-dimensional (2D) van der Waals (vdW) metal-semiconductor (MS) heterojunction as an efficient cold source (CS) has recently been proposed as a promising approach in the development of steep-slope field-effect transistors (FETs). In addition to the selection of source materials with linearly decreasing density-of-states-energy relations (D(E)s), in this study, we further verified, by means of a computer simulation, that a 2D semiconductor-semiconductor combination could also be used as an efficient CS. As a test case, a HfS2/MoTe2 FET was studied. It was found that MoTe2 can be spontaneously p-type-doped by interfacing with n-doped HfS2, resulting in a truncated decaying hot-carrier density with an increasing p-type channel barrier. Compared to the conventional MoTe2 FET, the subthreshold swing (SS) of the HfS2/MoTe2 FET can be significantly reduced to below 60 mV/decade, and the on-state current can be greatly enhanced by more than two orders of magnitude. It was found that there exists a hybrid transport mechanism involving the cold injection and the tunneling effect in such a p- and n-type HfS2/MoTe2 FET, which provides a new design insight into future low-power and high-performance 2D electronics from a physical point of view.

Case Report: Fanconi-Bickel Syndrome in a Chinese Girl With Diabetes and Severe Hypokalemia.

Fanconi-Bickel syndrome (FBS) is a rare autosomal recessive carbohydrate metabolism disorder. The main symptoms of FBS are hepatomegaly, nephropathy, postprandial hyperglycemia, fasting hypoglycemia, and growth retardation. Hypokalemia is a rare clinical feature in patients with FBS. In this study, we present a neonate suffering from FBS. She presented with hypokalemia, dysglycaemia, glycosuria, hepatomegaly, abnormality of liver function, and brain MRI. Trio whole-exome sequencing (WES) and Sanger sequencing were performed to identify the causal gene variants. A compound heterozygous mutation (NM_000340.2; p. Trp420*) of SLC2A2 was identified. Here, we report a patient with FBS in a consanguineous family with diabetes, severe hypokalemia, and other typical FBS symptoms. Patients with common clinical features may be difficult to diagnose just by phenotypes in the early stage of life, but WES could be an important tool. We also discuss the use of insulin in patients with FBS and highlight the importance of a continuous glucose monitoring system (CGMS), not only in diagnosis but also to avoid hypoglycemic events.

Risk factors for postoperative delirium in children with congenital heart disease: a prospective nested case-control study. / å ˆå¤©æ€§å¿ƒè„ç— æ‚£å„¿æœ¯åŽè°µå¦„å±é™©å› ç´ çš„å‰çž»æ€§å·¢å¼ç— ä¾‹å¯¹ç §ç ”ç©¶.

OBJECTIVES: To study the risk factors for postoperative delirium (POD) in children with congenital heart disease. METHODS: A prospective nested case-control study was performed on children with congenital heart disease who underwent surgery in Fuwai Hospital, Chinese Academy of Medical Sciences, from December 2020 to June 2021. The clinical data were compared between the POD group (n=114) and non-POD group (n=102). A multivariate unconditional logistic regression analysis was used to investigate the risk factors for POD in children with congenital heart disease. RESULTS: The multivariate logistic regression analysis showed that age (OR=0.951, P<0.001), gender (OR=2.127, P=0.049), number of invasive catheters per day (OR=1.490, P=0.017), degree of postoperative pain (OR=5.856, P<0.001), and preoperative parental anxiety level (OR=1.025, P=0.010) were independent risk factors for POD in children with congenital heart disease. CONCLUSIONS: The risk of POD increases in children with congenital heart disease who are younger, male, have higher number of invasive catheters per day, higher degree of postoperative pain, or higher preoperative parental anxiety level.

Eight-Component Nanoporous High-Entropy Oxides with Low Ru Contents as High-Performance Bifunctional Catalysts in Zn-Air Batteries.

One major challenge in heterogeneous catalysis is to reduce the usage of noble metals while maintaining the overall catalytic stability and efficiency in various chemical environments. In this work, a series of high-entropy catalysts are synthesized by a chemical dealloying method and find the increased entropy effect and non-noble metal contents would facilitate the formation of complete oxides with low crystallinity. Importantly, an optimal eight-component high-entropy oxide (HEO, Al-Ni-Co-Ru-Mo-Cr-Fe-Ti) is identified, which exhibits further enhanced catalytic activity for the oxygen evolution reaction (OER) as compared to the previously reported quinary AlNiCoRuMo and the widely-used commercial RuO2 catalysts, and at the same time similar catalytic activity for the oxygen reduction reaction (ORR) as the commercial Pt/C with a half-wave potential of 0.87 V. Such high-performance bi-functional catalysts, however, only require a half loading amount of Ru as compared to the quinary AlNiCoRuMo, due to the underlying Cr-Fe synergistic effects on tuning the electronic structures at active surface sites, as revealed by the first-principles density functional theory calculations of the authors. The eight-component HEO also demonstrates excellent stability under continuous electrochemical working conditions, suitable for a wide range of applications such as metal-air batteries.

In situ coupling of Ag nanoparticles with high-entropy oxides as highly stable bifunctional catalysts for wearable Zn-Ag/Zn-air hybrid batteries.

With the combination of the advantages of both Zn-Ag and Zn-air batteries, hybrid Zn-Ag/Zn-air batteries nevertheless suffer greatly from structural instability and activity degradation of the catalysts at the air electrodes. Herein, we introduce a scalable chemical dealloying procedure to synthesize mutually interacting and stable bifunctional catalysts, consisting of imbedded Ag nanoparticles for the oxygen reduction reaction (ORR) and quantitatively designed multicomponent high-entropy oxides (HEOs) for the oxygen evolution reaction (OER). The ORR performance and the Zn-Ag battery capacity can be precisely controlled by the content of Ag nanoparticles. Impressively, with a significantly low Ag content (∼9.13 wt%) in the bifunctional (AlNiCoFeCr)3O4/Ag, our hybrid Zn-Ag/Zn-air batteries using such catalysts are able to be continuously charged/discharged for more than 450 h and deliver a high energy density of 810 W h kg-1. We expect that these stabilized noble metals in HEO nanocomposites may work as multifunctional electrocatalysts in many other energy conversion devices.

A dual-task dual-domain model for blind MRI reconstruction.

MRI reconstruction is the key technology to accelerate MR acquisition. Recent cascade models have gained satisfactory results, however, they deeply rely on the known sample mask, which we call it mask prior. To restore the MR image without mask prior, we designed an auxiliary network to estimate the mask from sampled k-space data. Experimentally, the sample mask can be completely estimated by the proposed network and be used to input to the cascade models. Moreover, we rethink the MRI reconstruction model as a k-space inpainting task. A dual-domain cascade network, which utilized partial convolutional layers to inpaint features in k-space, was presented to restore the MR image. Without the mask prior, our blind reconstruction model demonstrates the best reconstruction ability in both 4x acceleration and 8x acceleration.

Dual-task ultrasound spine transverse vertebrae segmentation network with contour regularization.

3D ultrasound imaging has become one of the common diagnosis ways to assess scoliosis since it is radiation-free, real-time, and low-cost. Spine curvature angle measurement is an important step to assess scoliosis precisely. One way to calculate the angle is using the vertebrae features of the 2-D coronal images to identify the most tilted vertebrae. To do the measurement, the segmentation of the transverse vertebrae is an important step. In this paper, we propose a dual-task ultrasound transverse vertebrae segmentation network (D-TVNet) based on U-Net. First, we arrange an auxiliary shape regularization network to learn the contour segmentation of the bones. It improves the boundary segmentation and anti-interference ability of the U-Net by fusing some of the features of the auxiliary task and the main task. Then, we introduce the atrous spatial pyramid pooling (ASPP) module to the end of the down-sampling stage of the main task stream to improve the relative feature extraction ability. To further improve the boundary segmentation, we extendedly fuse the down-sampling output features of the auxiliary network in the ASPP. The experiment results show that the proposed D-TVNet achieves the best dice score of 86.68% and the mean dice score of 86.17% based on cross-validation, which is an improvement of 5.17% over the baseline U-Net. An automatic ultrasound spine bone segmentation network with promising results has been achieved.

First report of leaf spot disease caused by Colletotrichum siamense on Cornus hongkongensis (Hemsl.) in China.

Cornus hongkongensis (Hemsl.) is an excellent ornamental tree species in China and elsewhere. In 2019, C. hongkongensis anthracnose was firstly observed at the campus of Jiangxi Agricultural University (JXAU) (28°45'56″N, 115°50'21″E), then found in parks, Nanchang, China. In early August, the disease appeared and lasted until the leaves dropped (November). The disease incidence was above 60%, and the diseased leaf rate was above 70%. The lesions mostly appeared along the leaf edges. Some small round to irregular lesions also developed in other parts of the leaves. These diseased leaves had circular or irregularly shaped spots with gray-white color in the center and dark brown on the edge of the lesions. Later, the lesions became necrotic and shriveled. As the disease progressed, the spots coalesced so that affected leaves appeared blighted (Supplementary Figure 1 A-C). To identify the pathogen, leaves with typical symptoms from the campus of JXAU were collected and small pieces (5 × 5 mm) from the lesion borders were surfaced sterilized in 70% ethanol for 30 s, followed by 1 min in 3% NaOCl, and then rinsed with sterile distilled water three times. Leaf pieces were placed on potato dextrose agar (PDA) and incubated at 25 °C under a 12-h light/dark cycle (3000 lx). Pure cultures were obtained from individual conidia by single spore isolates. For studies of microscopic morphology, a representative isolate JX-S4 was subcultured on PDA. The colony of JX-S4 was white and turning gray and light gray on the reverse side, producing dark-green pigmentation near the center (Supplementary Figure 1 D). The conidia were one-celled, straight, hyaline, subcylindrical with rounded ends and 16.9 ± 1.6 × 6.0 ± 0.6 µm (n = 50) in size. Appressoria were one-celled, pale brown, thick-walled, ellipsoidal, and measured 8.7 ± 1.7 × 6.4 ± 0.8 µm (n = 50) (Supplementary Figure 1 E, F). The morphological characteristics of JX-S4 matched those of the Colletotrichum siamense species (Weir et al. 2012). For accurate identification, the internal transcribed spacer (ITS) and the genes encoding glyceraldehyde-3-phosphate dehydrogenase (GAPDH), chitin synthase (CHS-I), beta-tubulin 2 (TUB2), and calmodulin (CAL) were respectively amplified with primers ITS1/ITS4, GDF/GDR, CHS-79F/CHS-345R, ßt2a/ßt2b, and CL1/CL2. The sequences were deposited in GenBank (Accession nos. MT587807, MT628710, MT628709, MT628711, and MT628708). Phylogenetic analysis was calculated with concatenated sequences (ITS, GAPDH, CHS-I, CAL, and TUB2) using MEGA 7. In the maximum likelihood phylogenetic tree, Isolate JX-S4 was clustered with C. siamense with 93% bootstrap support (Supplementary Figure 2). Based on the morphological characteristics and phylogenetic analysis, JX-S4 was identified as C. siamense. Pathogenicity test of JX-S4 was verified on 45 attached healthy leaves from three C. hongkongensis plants (10-year-old) at the campus of JXAU inoculated with mycelial plugs (φ=5 mm) from the culture edge (6-day-old) on PDA. And an additional 45 healthy leaves were inoculated with PDA plugs as controls. The leaves were wounded with a red-hot needle (φ=0.5 mm). All treatment and control leaves were wrapped up with black plastic bags to keep them moist for 2 days. The pathogenicity tests were repeated twice. Within 7 days, all the inoculated leaves developed the lesions, which were similar to those observed in the field. Control leaves were asymptomatic (Supplementary Figure 1 G, H). The same fungus was re-isolated from the symptomatic tissues, fulfilling Koch's postulates. To our knowledge, this is the first report of C. siamense causing C. hongkongensis anthracnose. This finding provides crucial information for managing this disease. For example, when diagnosing Cornus anthracnose, C. siamense needs to be looked out for and appropriate control measures implemented.

Ultrasound volume projection image quality selection by ranking from convolutional RankNet.

Periodic inspection and assessment are important for scoliosis patients. 3D ultrasound imaging has become an important means of scoliosis assessment as it is a real-time, cost-effective and radiation-free imaging technique. With the generation of a 3D ultrasound volume projection spine image using our Scolioscan system, a series of 2D coronal ultrasound images are produced at different depths with different qualities. Selecting a high quality image from these 2D images is the crucial task for further scoliosis measurement. However, adjacent images are similar and difficult to distinguish. To learn the nuances between these images, we propose selecting the best image automatically, based on their quality rankings. Here, the ranking algorithm we use is a pairwise learning-to-ranking network, RankNet. Then, to extract more efficient features of input images and to improve the discriminative ability of the model, we adopt the convolutional neural network as the backbone due to its high power of image exploration. Finally, by inputting the images in pairs into the proposed convolutional RankNet, we can select the best images from each case based on the output ranking orders. The experimental result shows that convolutional RankNet achieves better than 95.5% top-3 accuracy, and we prove that this performance is beyond the experience of a human expert.

Automatic Segmentation of 3D Ultrasound Spine Curvature Using Convolutional Neural Network.

Scoliosis is a 3D spinal deformation where the spine takes a lateral curvature, which generates an angle in a coronal plane. For periodic detection of scoliosis, safe and economic imaging modality is needed as continuous exposure to radiative imaging may cause cancer. 3D ultrasound imaging is a cost-effective and radiation-free imaging modality which gives volume projection image. Identification of mid-spine line using manual, semi-automatic and automatic methods have been published. Still, there are some difficulties like variations in human measurement, slow processing of data associated with them. In this paper, we propose an unsupervised ground truth generation and automatic spine curvature segmentation using U- Net. This approach of the application of Convolutional Neural Network on ultrasound spine image, to perform automatic detection of scoliosis, is a novel one.

Ultrahigh drive current and large selectivity in GeS selector.

Selector devices are indispensable components of large-scale nonvolatile memory and neuromorphic array systems. Besides the conventional silicon transistor, two-terminal ovonic threshold switching device with much higher scalability is currently the most industrially favored selector technology. However, current ovonic threshold switching devices rely heavily on intricate control of material stoichiometry and generally suffer from toxic and complex dopants. Here, we report on a selector with a large drive current density of 34 MA cm-2 and a ~106 high nonlinearity, realized in an environment-friendly and earth-abundant sulfide binary semiconductor, GeS. Both experiments and first-principles calculations reveal Ge pyramid-dominated network and high density of near-valence band trap states in amorphous GeS. The high-drive current capacity is associated with the strong Ge-S covalency and the high nonlinearity could arise from the synergy of the mid-gap traps assisted electronic transition and local Ge-Ge chain growth as well as locally enhanced bond alignment under high electric field.

Multi-Level Cross Residual Network for Lung Nodule Classification.

Computer-aided algorithm plays an important role in disease diagnosis through medical images. As one of the major cancers, lung cancer is commonly detected by computer tomography. To increase the survival rate of lung cancer patients, an early-stage diagnosis is necessary. In this paper, we propose a new structure, multi-level cross residual convolutional neural network (ML-xResNet), to classify the different types of lung nodule malignancies. ML-xResNet is constructed by three-level parallel ResNets with different convolution kernel sizes to extract multi-scale features of the inputs. Moreover, the residuals are connected not only with the current level but also with other levels in a crossover manner. To illustrate the performance of ML-xResNet, we apply the model to process ternary classification (benign, indeterminate, and malignant lung nodules) and binary classification (benign and malignant lung nodules) of lung nodules, respectively. Based on the experiment results, the proposed ML-xResNet achieves the best results of 85.88% accuracy for ternary classification and 92.19% accuracy for binary classification, without any additional handcrafted preprocessing algorithm.

A New Opportunity for 2D van der Waals Heterostructures: Making Steep-Slope Transistors.

The use of a foreign metallic cold source (CS) has recently been proposed as a promising approach toward the steep-slope field-effect-transistor (FET). In addition to the selection of source material with desired density of states-energy relation (D(E)), engineering the source:channel interface for gate-tunable channel-barrier is crucial to CS-FETs. However, conventional metal:semiconductor (MS) interfaces generally suffer from strong Fermi-level pinning due to the inevitable chemical disorder and defect-induced gap states, precluding the gate tunability of the barriers. By comprehensive materials and device modeling at the atomic scale, it is reported that 2D van der Waals (vdW) MS interfaces, with their atomic sharpness and cleanness, can be considered as general ingredients for CS-FETs. As test cases, InSe-based n-type FETs are studied. It is found that graphene can be spontaneously p-type doped along with slightly opened bandgap around the Dirac-point by interfacing with InSe, resulting in superexponentially decaying hot carrier density with increasing n-type channel-barrier. Moreover, the D(E) relations suggest that 2D transition-metal dichalcogenides and 2D transition-metal carbides are a rich library of CS materials. Graphene, Cd3 C2 , T-VTe2 , H-VTe2 , and H-TaTe2 CSs lead to subthreshold swing below 60 mV dec-1 . This work broadens the application potentials of 2D vdW MS heterostructures and serves as a springboard for more studies on low-power electronics based on 2D materials.

Classification of EEG Motor Imagery Tasks Using Convolution Neural Networks.

Electroencephalograph (EEG) is a highly nonlinear data and very difficult to be classified. The EEG signal is commonly used in the area of Brain-Computer Interface (BCI). The signal can be used as an operative command for directional movements for a powered wheelchair to assist people with disability in performing the daily activity.In this paper, we aim to classify Electroencephalograph EEG signals extracted from subjects which had been trained to perform four Motoric Imagery (MI) tasks for two classes. The classification will be processed via a Convolutional Neural Network (CNN) utilising all 22 electrodes based on 10-20 system placement. The EEG datasets will be transformed into scaleogram using Continuous Wavelet Transform (CWT) method.We evaluated two different types of image configuration, i.e. layered and stacked input datasets. Our procedure starts from denoising the EEG signals, employing Bump CWT from 8-32 Hz brain wave. Our CNN architecture is based on the Visual Geometry Group (VGG-16) network. Our results show that layered image dataset yields a high accuracy with an average of 68.33% for two classes classification.

3D Ultrasound Spine Image Selection Using Convolution Learning-to-Rank Algorithm.

3D Ultrasound imaging has become an important means of scoliosis assessment as it is a real-time, cost-effective and radiation-free imaging technique. However, the coronal images from different depths of a 3D ultrasound image have different imaging definitions. So there is a need to select the coronal image that would give the best image definition. Also, manual selection of coronal images is time-consuming and limited to the discretion and capability of the assessor. Therefore, in this paper, we have developed a convolution learning-to-rank algorithm to select the best ultrasound images automatically using raw ultrasound images. The ranking is done based on the curve angle of the spinal cord. Firstly, we approached the image selection problem as a ranking problem; ranked based on probability of an image to be a good image. Here, we use the RankNet, a pairwise learning-to-rank method, to rank the images automatically. Secondly, we replaced the backbone of the RankNet, which is the traditional artificial neural network (ANN), with convolution neural network (CNN) to improve the feature extracting ability for the successive iterations. The experimental result shows that the proposed convolutional RankNet achieves the perfect accuracy of 100% while conventional DenseNet achieved 35% only. This proves that the convolutional RankNet is more suitable to highlight the best quality of ultrasound image from multiple mediocre ones.

Lung Nodule Classification using A Novel Two-stage Convolutional Neural Networks Structure'.

Lung cancer is one of the most fatal cancers in the world. If the lung cancer can be diagnosed at an early stage, the survival rate of patients post treatment increases dramatically. Computed Tomography (CT) diagram is an effective tool to detect lung cancer. In this paper, we proposed a novel two-stage convolution neural network (2S-CNN) to classify the lung CT images. The structure is composed of two CNNs. The first CNN is a basic CNN, whose function is to refine the input CT images to extract the ambiguous CT images. The output of first CNN is fed into another inception CNN, a simplified version of GoogLeNet, to enhance the better recognition on complex CT images. The experimental results show that our 2S-CNN structure has achieved an accuracy of 89.6%.