AI Medical Compendium Topic:
Semantics

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Is Context-Aware CNN Ready for the Surroundings? Panoramic Semantic Segmentation in the Wild.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Semantic segmentation, unifying most navigational perception tasks at the pixel level has catalyzed striking progress in the field of autonomous transportation. Modern Convolution Neural Networks (CNNs) are able to perform semantic segmentation both ...

Few-Shot Human-Object Interaction Recognition With Semantic-Guided Attentive Prototypes Network.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Extreme instance imbalance among categories and combinatorial explosion make the recognition of Human-Object Interaction (HOI) a challenging task. Few studies have addressed both challenges directly. Motivated by the success of few-shot learning that...

Text Semantic Classification of Long Discourses Based on Neural Networks with Improved Focal Loss.

Computational intelligence and neuroscience
Semantic classification of Chinese long discourses is an important and challenging task. Discourse text is high-dimensional and sparse. Furthermore, when the number of classes of dataset is large, the data distribution will be seriously imbalanced. I...

Gender Stereotypes in Natural Language: Word Embeddings Show Robust Consistency Across Child and Adult Language Corpora of More Than 65 Million Words.

Psychological science
Stereotypes are associations between social groups and semantic attributes that are widely shared within societies. The spoken and written language of a society affords a unique way to measure the magnitude and prevalence of these widely shared colle...

Multi-Scale Self-Guided Attention for Medical Image Segmentation.

IEEE journal of biomedical and health informatics
Even though convolutional neural networks (CNNs) are driving progress in medical image segmentation, standard models still have some drawbacks. First, the use of multi-scale approaches, i.e., encoder-decoder architectures, leads to a redundant use of...

Ontological representation, classification and data-driven computing of phenotypes.

Journal of biomedical semantics
BACKGROUND: The successful determination and analysis of phenotypes plays a key role in the diagnostic process, the evaluation of risk factors and the recruitment of participants for clinical and epidemiological studies. The development of computable...

Domain specific word embeddings for natural language processing in radiology.

Journal of biomedical informatics
BACKGROUND: There has been increasing interest in machine learning based natural language processing (NLP) methods in radiology; however, models have often used word embeddings trained on general web corpora due to lack of a radiology-specific corpus...

Towards semantic interoperability: finding and repairing hidden contradictions in biomedical ontologies.

BMC medical informatics and decision making
BACKGROUND: Ontologies are widely used throughout the biomedical domain. These ontologies formally represent the classes and relations assumed to exist within a domain. As scientific domains are deeply interlinked, so too are their representations. W...

Quality assurance and enrichment of biological and biomedical ontologies and terminologies.

BMC medical informatics and decision making
Biological and biomedical ontologies and terminologies are used to organize and store various domain-specific knowledge to provide standardization of terminology usage and to improve interoperability. The growing number of such ontologies and termino...

KGen: a knowledge graph generator from biomedical scientific literature.

BMC medical informatics and decision making
BACKGROUND: Knowledge is often produced from data generated in scientific investigations. An ever-growing number of scientific studies in several domains result into a massive amount of data, from which obtaining new knowledge requires computational ...