How to automatically detect intracranial aneurysms from Three-Dimension Time of Flight Magnetic Resonance Angiography images is a typical 3D image classification problem. Currently, the commonly used method is the Maximum Intensity Projection- based way. It transfers 3D classification into 2D case by projecting the 3D patch into 2D planes along different directions on the basis of voxel’s intensity. After then, the 2D Convolutional Neural Network is established to do classification. It has been shown that the MIP-based method can reduce the (...) demands for the samples and increase the computation efficiency. Meanwhile, the accuracy is comparable with that of 3D image classification. Inspired by the strategy of MIP, we want to further reduce the demands for samples and accelerate the training by transferring the 2D image classification into 1D case, i.e., we want to generate the 1D vectors from the MIP images and then establish a 1D CNN to do intracranial aneurysm detection and classification for 3D TOF MRA image. Specifically, our method first extracts a series of patches as the Region of Interests along the blood vessels from the original 3D TOF MRA 3D image. The corresponding MIP images of each ROI will be obtained through maximum intensity projecting. Then, we generate a series of 1D vectors by accumulating each MIP image along different directions. Meanwhile, a 1D CNN is established to detect aneurysms, in which, the input is the obtained 1D vectors and the output is the binary classification result denoting whether there are intracranial aneurysms in the considered patch. Generally, compared with 2D- and 3D-CNN, the 1D CNN-based way greatly accelerates the training and shows stronger robustness in the case of fewer samples. The efficiency of the proposed method outperforms the 2D CNN about 10 times in CPU training. Yet, their accuracies are close. (shrink)

This study investigated eye-movement patterns during emotion perception for children with hearing aids and hearing children. Seventy-eight participants aged from 3 to 7 were asked to watch videos with a facial expression followed by an oral statement, and these two cues were either consistent or inconsistent in emotional valence. Results showed that while normal-hearing children paid more attention to the upper part of the face, children with hearing aids paid more attention to the lower face after the oral statement was (...) presented. When there was an inconsistency between the visual and auditory cues, children with hearing aids were likely to increase attention to the upper face. The results revealed the underlying mechanism of deficits in emotion perception for hearing-impaired children. (shrink)

现代医学在技术上高度发展,却与因人文精神的失落造成的职业冷漠形成巨大反差。本书呼吁对医学的功能与本质进行反思,呼唤医学保持应有的人性温度。.

现代医学在技术上高度发展,却与因人文精神的失落造成的职业冷漠形成巨大反差。本书呼吁对医学的功能与本质进行反思,呼唤医学保持应有的人性温度。.

This study aimed to explore the association between the functioning of family environment and academic burnout of Chinese college students as well as the mediating effects of the interpersonal resource and intrapersonal resource [i.e., positive psychological capital ] in this relationship. A total of 1971 Chinese undergraduates were involved in an online questionnaire survey and data analysis. It was found that family cohesion and adaptability was negatively related to academic burnout. Mediation analyses demonstrated that family cohesion and adaptability did not (...) directly affect academic burnout, but indirectly through increasing PsyCap, and through enhancing peer support and then increasing PsyCap in serial. Meanwhile, the relationship between family cohesion and adaptability and academic burnout was not mediated by peer support alone. These findings highlight the family environment’s crucial role in youth mental health and positive development in the college context. (shrink)

Aiming at the problem that the embedded platform cannot meet the real-time detection of multisource images, this paper proposes a lightweight target detection network MNYOLO suitable for embedded platforms using deep separable convolution instead of standard convolution to reduce the number of model parameters and calculations; at the same time, the visible light target detection model is used as the pretraining model of the infrared target detection model and the infrared target data set collected on the spot is fine-tuned to (...) obtain the infrared target detection model. On this basis, a decision-level fusion detection model is obtained to realize the complementary information of infrared and visible light multiband information. The experimental results show that it has a more obvious advantage in detection accuracy than the single-band target detection model while the decision-level fusion target detection model meets the real-time requirements and also verifies the effectiveness of the above algorithm. (shrink)