The accurate segmentation of breast ultrasound images is an important precondition for the lesion determination. The existing segmentation approaches embrace massive parameters, sluggish inference speed, and huge memory consumption. To tackle this problem, we propose T2KD Attention U-Net (dual-Teacher Knowledge Distillation Attention U-Net), a lightweight semantic segmentation method combined double-path joint distillation in breast ultrasound images. Primarily, we designed two teacher models to learn the fine-grained features from each class of images according to different feature representation and semantic information of benign and malignant breast lesions. Then we leveraged the joint distillation to train a lightweight student model. Finally, we constructed a novel weight balance loss to focus on the semantic feature of small objection, solving the unbalance problem of tumor and background. Specifically, the extensive experiments conducted on Dataset BUSI and Dataset B demonstrated that the T2KD Attention U-Net outperformed various knowledge distillation counterparts. Concretely, the accuracy, recall, precision, Dice, and mIoU of proposed method were 95.26%, 86.23%, 85.09%, 83.59%and 77.78% on Dataset BUSI, respectively. And these performance indexes were 97.95%, 92.80%, 88.33%, 88.40% and 82.42% on Dataset B, respectively. Compared with other models, the performance of this model was significantly improved. Meanwhile, compared with the teacher model, the number, size, and complexity of student model were significantly reduced (2.2×106 vs. 106.1×106, 8.4 MB vs. 414 MB, 16.59 GFLOPs vs. 205.98 GFLOPs, respectively). Indeedy, the proposed model guarantees the performances while greatly decreasing the amount of computation, which provides a new method for the deployment of clinical medical scenarios.
Attention level evaluation refers to the evaluation of people's attention level through observation or experimental testing, and its research results have great application value in education and teaching, intelligent driving, medical health and other fields. With its objective reliability and security, electroencephalogram signals have become one of the most important technical means to analyze and express attention level. At present, there is little review literature that comprehensively summarize the application of electroencephalogram signals in the field of attention evaluation. To this end, this paper first summarizes the research progress on attention evaluation; then the important methods for electroencephalogram attention evaluation are analyzed, including data preprocessing, feature extraction and selection, attention evaluation methods, etc.; finally, the shortcomings of the current development in the field of electroencephalogram attention evaluation are discussed, and the future development trend is prospected, to provide research references for researchers in related fields.
To accurately capture and effectively integrate the spatiotemporal features of electroencephalogram (EEG) signals for the purpose of improving the accuracy of EEG-based emotion recognition, this paper proposes a new method combining independent component analysis-recurrence plot with an improved EfficientNet version 2 (EfficientNetV2). First, independent component analysis is used to extract independent components containing spatial information from key channels of the EEG signals. These components are then converted into two-dimensional images using recurrence plot to better extract emotional features from the temporal information. Finally, the two-dimensional images are input into an improved EfficientNetV2, which incorporates a global attention mechanism and a triplet attention mechanism, and the emotion classification is output by the fully connected layer. To validate the effectiveness of the proposed method, this study conducts comparative experiments, channel selection experiments and ablation experiments based on the Shanghai Jiao Tong University Emotion Electroencephalogram Dataset (SEED). The results demonstrate that the average recognition accuracy of our method is 96.77%, which is significantly superior to existing methods, offering a novel perspective for research on EEG-based emotion recognition.
ObjectiveTo observe the effect of sensory integration training combined with methylphenidate hydrochloride on attention deficit hyperactivity disorder (ADHD). MethodsThe clinical data of 96 patients with ADHD diagnosed between January 2009 and March 2013 were retrospectively analyzed. The patients were divided into two groups by the table of random number. The trail group (n=48) received the combination therapy of sensory integration training combined with methylphenidate hydrochloride; while the control group (n=48) only received the medication of methylphenidate hydrochloride. The scores of sensory integration ability rating scale, integrated visual and auditory continuous performance test (IVA-CPT), Conner's behavior rating scale, Chinese Wechsler Intelligence Scale for Children (C-WISC) and adverse reactions were observed and compared between the two groups. ResultsThe scores of the sensory integration ability rating scale, FRCQ, FAQ (IVA-CPT), PIQ, VIQ, FIQ, C factor (C-WISC) in both of the two groups were significantly higher after the therapy; while the scores of the study, behavior, somatopsychic disturbance, impulsion, hyperactivity index and anxiety factor significantly decreased after the treatment (P<0.05). Compared with the control group, the trial group's scores of sensory integration ability rating scale, IVA-CPT, Conner's behavior rating scale, C-WISC were improved obviously, and the adverse reactions were significantly less (P<0.05). ConclusionThe sensory integration training combined with methylphenidate hydrochloride is sage and effective on children with attention deficit hyperactivity disorder.
Accurate segmentation of pediatric echocardiograms is a challenging task, because significant heart-size changes with age and faster heart rate lead to more blurred boundaries on cardiac ultrasound images compared with adults. To address these problems, a dual decoder network model combining channel attention and scale attention is proposed in this paper. Firstly, an attention-guided decoder with deep supervision strategy is used to obtain attention maps for the ventricular regions. Then, the generated ventricular attention is fed back to multiple layers of the network through skip connections to adjust the feature weights generated by the encoder and highlight the left and right ventricular areas. Finally, a scale attention module and a channel attention module are utilized to enhance the edge features of the left and right ventricles. The experimental results demonstrate that the proposed method in this paper achieves an average Dice coefficient of 90.63% in acquired bilateral ventricular segmentation dataset, which is better than some conventional and state-of-the-art methods in the field of medical image segmentation. More importantly, the method has a more accurate effect in segmenting the edge of the ventricle. The results of this paper can provide a new solution for pediatric echocardiographic bilateral ventricular segmentation and subsequent auxiliary diagnosis of congenital heart disease.
Glioma is a primary brain tumor with high incidence rate. High-grade gliomas (HGG) are those with the highest degree of malignancy and the lowest degree of survival. Surgical resection and postoperative adjuvant chemoradiotherapy are often used in clinical treatment, so accurate segmentation of tumor-related areas is of great significance for the treatment of patients. In order to improve the segmentation accuracy of HGG, this paper proposes a multi-modal glioma semantic segmentation network with multi-scale feature extraction and multi-attention fusion mechanism. The main contributions are, (1) Multi-scale residual structures were used to extract features from multi-modal gliomas magnetic resonance imaging (MRI); (2) Two types of attention modules were used for features aggregating in channel and spatial; (3) In order to improve the segmentation performance of the whole network, the branch classifier was constructed using ensemble learning strategy to adjust and correct the classification results of the backbone classifier. The experimental results showed that the Dice coefficient values of the proposed segmentation method in this article were 0.909 7, 0.877 3 and 0.839 6 for whole tumor, tumor core and enhanced tumor respectively, and the segmentation results had good boundary continuity in the three-dimensional direction. Therefore, the proposed semantic segmentation network has good segmentation performance for high-grade gliomas lesions.
Speech imagery is an emerging brain-computer interface (BCI) paradigm with potential to provide effective communication for individuals with speech impairments. This study designed a Chinese speech imagery paradigm using three clinically relevant words—“Help me”, “Sit up” and “Turn over”—and collected electroencephalography (EEG) data from 15 healthy subjects. Based on the data, a Channel Attention Multi-Scale Convolutional Neural Network (CAM-Net) decoding algorithm was proposed, which combined multi-scale temporal convolutions with asymmetric spatial convolutions to extract multidimensional EEG features, and incorporated a channel attention mechanism along with a bidirectional long short-term memory network to perform channel weighting and capture temporal dependencies. Experimental results showed that CAM-Net achieved a classification accuracy of 48.54% in the three-class task, outperforming baseline models such as EEGNet and Deep ConvNet, and reached a highest accuracy of 64.17% in the binary classification between “Sit up” and “Turn over”. This work provides a promising approach for future Chinese speech imagery BCI research and applications.
ObjectiveTo systematically review the effect of media multitasking on working memory and attention among adolescents. MethodsCNKI, CBM, WanFang Data, VIP, PubMed, Web of Science, and EMbase databases were electronically searched to collect cross-sectional studies on the effect of media multitasking on working memory and attention among adolescents from inception to January 1st, 2021. Two reviewers independently screened literature, extracted data, and assessed the risk of bias of included studies; then, meta-analysis was performed using Stata 15.1 software. ResultsA total of 16 cross-sectional studies were included. The results of meta-analysis showed that there were negative correlations between media multitasking and working memory (Cohen's d=0.40, 95%CI 0.14 to 0.66, P＝0.003), as well as in attention (Cohen's d=1.02, 95%CI 0.58 to 1.47, P<0.001). ConclusionCurrent evidence shows that media multitasking has negative impact on working memory and attention. Due to limited quality and quantity of the included studies, more high-quality studies are required to verify the above conclusion.
The conventional fault diagnosis of patient monitors heavily relies on manual experience, resulting in low diagnostic efficiency and ineffective utilization of fault maintenance text data. To address these issues, this paper proposes an intelligent fault diagnosis method for patient monitors based on multi-feature text representation, improved bidirectional gate recurrent unit (BiGRU) and attention mechanism. Firstly, the fault text data was preprocessed, and the word vectors containing multiple linguistic features was generated by linguistically-motivated bidirectional encoder representation from Transformer. Then, the bidirectional fault features were extracted and weighted by the improved BiGRU and attention mechanism respectively. Finally, the weighted loss function is used to reduce the impact of class imbalance on the model. To validate the effectiveness of the proposed method, this paper uses the patient monitor fault dataset for verification, and the macro F1 value has achieved 91.11%. The results show that the model built in this study can realize the automatic classification of fault text, and may provide assistant decision support for the intelligent fault diagnosis of the patient monitor in the future.
Objective To explore the white matter microstructural abnormalities in patients with different subtypes of attention-deficit/hyperactivity disorder (ADHD) and establish a diagnostic classification model. Methods Patients with ADHD admitted to West China Hospital of Sichuan University between January 2019 and September 2021 and healthy controls recruited through advertisement were prospectively selected. All participants underwent diffusion tensor imaging scanning. The whole brain voxel-based analysis was used to compare the diffusion parameter maps of fractional anisotropy (FA) among patients with combined subtype of ADHD (ADHD-C), patients with inattentive subtype of ADHD (ADHD-I) and healthy controls. The support vector machine classifier and feature selection method were used to construct the individual ADHD diagnostic classification model and efficiency was evaluated between each two groups of the ADHD patients and healthy controls. Results A total of 26 ADHD-C patients, 24 ADHD-I patients and 26 healthy controls were included. The three groups showed significant differences in FA values in the bilateral sagittal stratum of temporal lobe (ADHD-C<ADHD-I<healthy controls) and the isthmus of corpus callosum (ADHD-C>ADHD-I>healthy controls) (P<0.005). The direct comparison between the two subtypes of ADHD showed that ADHD-C had higher FA than ADHD-I in the right middle frontal gyrus. The classification model differentiating ADHD-C and ADHD-I showed the highest efficiency, with a total accuracy of 76.0%, sensitivity of 88.5%, and specificity of 70.8%. Conclusions There is both commonality and heterogeneity in white matter microstructural alterations in the two subtypes of patients with ADHD. The white matter damage of the sagittal stratum of temporal lobe and the corpus callosum may be the intrinsic pathophysiological basis of ADHD, while the anomalies of frontal brain region may be the differential point between different subtypes of patients.