In order to address the issues of spatial induction bias and lack of effective representation of global contextual information in colon polyp image segmentation, which lead to the loss of edge details and mis-segmentation of lesion areas, a colon polyp segmentation method that combines Transformer and cross-level phase-awareness is proposed. The method started from the perspective of global feature transformation, and used a hierarchical Transformer encoder to extract semantic information and spatial details of lesion areas layer by layer. Secondly, a phase-aware fusion module (PAFM) was designed to capture cross-level interaction information and effectively aggregate multi-scale contextual information. Thirdly, a position oriented functional module (POF) was designed to effectively integrate global and local feature information, fill in semantic gaps, and suppress background noise. Fourthly, a residual axis reverse attention module (RA-IA) was used to improve the network's ability to recognize edge pixels. The proposed method was experimentally tested on public datasets CVC-ClinicDB, Kvasir, CVC-ColonDB, and EITS, with Dice similarity coefficients of 94.04%, 92.04%, 80.78%, and 76.80%, respectively, and mean intersection over union of 89.31%, 86.81%, 73.55%, and 69.10%, respectively. The simulation experimental results show that the proposed method can effectively segment colon polyp images, providing a new window for the diagnosis of colon polyps.
Humans ; Colonic Polyps/diagnostic imaging* ; Computer Simulation ; Electric Power Supplies ; Semantics ; Image Processing, Computer-Assisted

Based on the top-level ontology and the existing ontology methodology, the related concepts of meridians and acupoints were discriminated, defined and classified; the relationship of core concepts were established, e.g. meridians, acupoints and zangfu. It was attempted to build an ontological semantic framework of meridians and acupoints. Through the investigation on the classification mode of the top-level ontology, it is proposed that the meridians and acupoints, as the unique concepts of traditional Chinese medicine, exist in the form of "emptiness" and belong to "immaterial entity". Meridians refer to the three-dimensional channels in the human body, and acupoints are divided into ontological acupoints and body surface ones. Ontological acupoints are regarded as a three-dimensional structure within the human body, whereas, body surface ones are the optimal sites for acupuncture needle insertion on the body surface, meaning, the zero-dimensional point on the body surface. The main relationships between meridians and acupoints include is-a, exterior-interior, located-in, correspondent-to, mapping, etc. The exploration of the semantic framework of meridians and acupoints is conductive to understanding the connotation of meridians, acupoints and their relationship.
Acupuncture ; Acupuncture Points ; Acupuncture Therapy/methods* ; Humans ; Meridians ; Semantics

The past twenty years have seen the increasingly important role of ontology in traditional Chinese medicine (TCM). However, the development of TCM ontology faces many challenges. Since the epistemologies dramatically differ between TCM and contemporary biomedicine, it is hard to apply the existing top-level ontology mechanically. "Data silos" are widely present in the currently available terminology standards, term sets, and ontologies. The formal representation of ontology needs to be further improved in TCM. Therefore, we propose a unified basic semantic framework of TCM based on in-depth theoretical research on the existing top-level ontology and a re-study of important concepts in TCM. Under such a framework, ontologies in TCM sub-domains should be built collaboratively and be represented formally in a common format. Besides, extensive cooperation should be encouraged by establishing ontology research communities to promote ontology peer review and reuse.
Medicine, Chinese Traditional ; Semantics ; Drugs, Chinese Herbal

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.
Attention ; Glioma/diagnostic imaging* ; Humans ; Magnetic Resonance Imaging/methods* ; Semantics

Ultrasonic examination is a common method in thyroid examination, and the results are mainly composed of thyroid ultrasound images and text reports. Implementation of cross modal retrieval method of images and text reports can provide great convenience for doctors and patients, but currently there is no retrieval method to correlate thyroid ultrasound images with text reports. This paper proposes a cross-modal method based on the deep learning and improved cross-modal generative adversarial network: ①the weight sharing constraints between the fully connection layers used to construct the public representation space in the original network are changed to cosine similarity constraints, so that the network can better learn the common representation of different modal data; ②the fully connection layer is added before the cross-modal discriminator to merge the full connection layer of image and text in the original network with weight sharing. Semantic regularization is realized on the basis of inheriting the advantages of the original network weight sharing. The experimental results show that the mean average precision of cross modal retrieval method for thyroid ultrasound image and text report in this paper can reach 0.508, which is significantly higher than the traditional cross-modal method, providing a new method for cross-modal retrieval of thyroid ultrasound image and text report.
Humans ; Image Processing, Computer-Assisted ; Semantics ; Thyroid Gland

Semantic processing plays a key role in the language comprehension. In the past more than two decades, by using different kinds of cognitive neuroscience methods, the time course and the neural basis of semantic processing had been comprehensively investigated, and a few classic models had been proposed based on these studies. Future studies should focus more on a precise and systematic definition of the concept of semantic processing. Furthermore, by using the convergence evidence from multi-methodologies, more systematic studies on semantic processing should be carried out from the perspective of the wholeness and the interactivity of the brain function to understand the processes.
Brain ; physiology ; Brain Mapping ; Comprehension ; Humans ; Language ; Semantics

Words denoting abstract concepts constitute nearly half of human lexicon and serve as building blocks of the human culture. Since the advent of non-invasive neuroimaging techniques, great progress has been made in revealing the neurobiological foundation of concrete object and action concepts, yet it remains unclear how abstract concepts are stored and processed in the brain. Here we review recent development in this field, focusing on both theoretical perspectives and neuroimaging findings. We found that abstract concepts can be represented via linguistic and experiential information; the neural correlates of abstract concepts are partly in line with such a theoretical framework. Future studies are warranted to uncover the cognitive and neural mechanisms of language and experience in abstract word representation, which will help to deepen our understanding of general computational principles of the human conceptual system and to promote the development of the brain-like artificial intelligence.
Brain ; physiology ; Cognition ; Concept Formation ; Humans ; Language ; Semantics

BACKGROUND: Eczema is the most common skin problem among children in Hong Kong. Previous studies have highlighted that the quality of life of the families of children with eczema influences the effects of eczema interventions. However, the Chinese version of the Family Dermatology Life Quality Index (C-FDLQI), a tool for measuring the quality of life of the families of children with eczema, has not yet been validated. OBJECTIVE: This study examined the psychometric properties of the C-FDLQI among parents and caregivers of children with eczema in Hong Kong. METHODS: This study evaluated the internal consistency, test-retest reliability and structural validity of the C-FDLQI and its convergent validity by examining its correlations with the SCORing Atopic Dermatitis (SCORAD) and the Cantonese version of the Children’s Dermatology Life Quality Index (C-CDLQI) among 147 Chinese parents/caregivers of children with varying degrees of eczema.
Asian Continental Ancestry Group ; Caregivers ; Child ; Dermatitis, Atopic ; Dermatology ; Eczema ; Hong Kong ; Humans ; Parents ; Psychometrics ; Quality of Life ; Reproducibility of Results ; Semantics ; Skin

BACKGROUND AND PURPOSE: Semantic verbal fluency test is a neuropsychological assessment that can sensitively detect neuropathological changes. Considering its multifactorial features tapping various cognitive domains such as semantic memory, executive function, and working memory, it is necessary to examine verbal fluency performance in association with underlying cognitive functions. The objective of the current study was to investigate semantic fluency patterns of people with mild cognitive impairment (MCI) based on clustering and switching and their relationship with working memory. METHODS: Twenty-six individuals with MCI and 23 normal elderly adults participated in this study. A semantic verbal fluency test (animal version) was administered and the performance was analyzed using the following measures: number of correct words, cluster size, and number of switches. Scores of digit forward (DF) and backward span tasks were employed as working memory measures. RESULTS: Analyses of variance revealed significant group differences in the numbers of correct words and switches. Multivariate logistic regression and receiver-operating characteristic analyses showed that the number of switches more sensitively distinguished MCI existence than the number of correct words. Stepwise linear regression analysis showed that DF task and age significantly predicted the number of correct words while only the DF task significantly predicted the number of switches. CONCLUSIONS: Decrement in semantic verbal fluency in MCI seems to be associated with impaired switching abilities. Working memory capacity might serve as the underlying cognitive factor related to decreased verbal fluency in MCI.
Adult ; Aged ; Cluster Analysis ; Cognition ; Executive Function ; Humans ; Linear Models ; Logistic Models ; Memory ; Memory, Short-Term ; Mild Cognitive Impairment ; Semantics

BACKGROUND AND PURPOSE: In Alzheimer's continuum (a comprehensive of preclinical Alzheimer's disease [AD], mild cognitive impairment [MCI] due to AD, and AD dementia), cognitive dysfunctions are often related to cortical atrophy in specific brain regions. The purpose of this study was to investigate the association between anatomical pattern of cortical atrophy and specific neuropsychological deficits. METHODS: A total of 249 participants with Alzheimer's continuum (125 AD dementia, 103 MCI due to AD, and 21 preclinical AD) who were confirmed to be positive for amyloid deposits were collected from the memory disorder clinic in the department of neurology at Samsung Medical Center in Korea between September 2013 and March 2018. To analyze neuropsychological test-specific neural correlates representing the relationship between cortical atrophy measured by cortical thickness and performance in specific neuropsychological tests, a linear regression analysis was performed. Two neural correlates acquired by 2 different standardized scores in neuropsychological tests were also compared. RESULTS: Cortical atrophy in several specific brain regions was associated with most neuropsychological deficits, including digit span backward, naming, drawing-copying, verbal and visual recall, semantic fluency, phonemic fluency, and response inhibition. There were a few differences between 2 neural correlates obtained by different z-scores. CONCLUSIONS: The poor performance of most neuropsychological tests is closely related to cortical thinning in specific brain areas in Alzheimer's continuum. Therefore, the brain atrophy pattern in patients with Alzheimer's continuum can be predict by an accurate analysis of neuropsychological tests in clinical practice.
Alzheimer Disease ; Atrophy ; Brain ; Cognition ; Dementia ; Humans ; Korea ; Linear Models ; Memory Disorders ; Mild Cognitive Impairment ; Neuroanatomy ; Neurology ; Neuropsychological Tests ; Plaque, Amyloid ; Semantics