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Semantics

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Semi-Supervised Pixel Contrastive Learning Framework for Tissue Segmentation in Histopathological Image.

IEEE journal of biomedical and health informatics
Accurate tissue segmentation in histopathological images is essential for promoting the development of precision pathology. However, the size of the digital pathological image is great, which needs to be tiled into small patches containing limited se...

A study on pharmaceutical text relationship extraction based on heterogeneous graph neural networks.

Mathematical biosciences and engineering : MBE
Effective information extraction of pharmaceutical texts is of great significance for clinical research. The ancient Chinese medicine text has streamlined sentences and complex semantic relationships, and the textual relationships may exist between h...

Text Sentiment Analysis Based on a New Hybrid Network Model.

Computational intelligence and neuroscience
The research of text sentiment analysis based on deep learning is increasingly rich, but the current models still have different degrees of deviation in understanding of semantic information. In order to reduce the loss of semantic information and im...

Improving fine-tuning of self-supervised models with Contrastive Initialization.

Neural networks : the official journal of the International Neural Network Society
Self-supervised learning (SSL) has achieved remarkable performance in pre-training the models that can be further used in downstream tasks via fine-tuning. However, these self-supervised models may not capture meaningful semantic information since th...

Klarigi: Characteristic explanations for semantic biomedical data.

Computers in biology and medicine
Annotation of biomedical entities with ontology classes provides for formal semantic analysis and mobilisation of background knowledge in determining their relationships. To date, enrichment analysis has been routinely employed to identify classes th...

Querying semantic catalogues of biomedical databases.

Journal of biomedical informatics
BACKGROUND: Secondary use of health data is a valuable source of knowledge that boosts observational studies, leading to important discoveries in the medical and biomedical sciences. The fundamental guiding principle for performing a successful obser...

Multi-Task Learning Model for Kazakh Query Understanding.

Sensors (Basel, Switzerland)
Query understanding (QU) plays a vital role in natural language processing, particularly in regard to question answering and dialogue systems. QU finds the named entity and query intent in users' questions. Traditional pipeline approaches manage the ...

Depression Detection Based on Hybrid Deep Learning SSCL Framework Using Self-Attention Mechanism: An Application to Social Networking Data.

Sensors (Basel, Switzerland)
In today's world, mental health diseases have become highly prevalent, and depression is one of the mental health problems that has become widespread. According to WHO reports, depression is the second-leading cause of the global burden of diseases. ...

Semantic-Powered Explainable Model-Free Few-Shot Learning Scheme of Diagnosing COVID-19 on Chest X-Ray.

IEEE journal of biomedical and health informatics
Chest X-ray (CXR) is commonly performed as an initial investigation in COVID-19, whose fast and accurate diagnosis is critical. Recently, deep learning has a great potential in detecting people who are suspected to be infected with COVID-19. However,...

Machine understanding surgical actions from intervention procedure textbooks.

Computers in biology and medicine
The automatic extraction of procedural surgical knowledge from surgery manuals, academic papers or other high-quality textual resources, is of the utmost importance to develop knowledge-based clinical decision support systems, to automatically execut...