AI Medical Compendium Topic:
Semantics

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Agent-Based Semantic Role Mining for Intelligent Access Control in Multi-Domain Collaborative Applications of Smart Cities.

Sensors (Basel, Switzerland)
Significance and popularity of Role-Based Access Control (RBAC) is inevitable; however, its application is highly challenging in multi-domain collaborative smart city environments. The reason is its limitations in adapting the dynamically changing in...

HiAM: A Hierarchical Attention based Model for knowledge graph multi-hop reasoning.

Neural networks : the official journal of the International Neural Network Society
Learning to reason in large-scale knowledge graphs has attracted much attention from research communities recently. This paper targets a practical task of multi-hop reasoning in knowledge graphs, which can be applied in various downstream tasks such ...

Knowledge-Powered Deep Breast Tumor Classification With Multiple Medical Reports.

IEEE/ACM transactions on computational biology and bioinformatics
Breast tumor classification with multiple medical reports such as B-ultrasound, Mammography (X-ray) and Nuclear Magnetic Resonance Imaging (MRI) is crucial to the intelligent cancer diagnosis system. Unlike the other domain texts, the medical reports...

ILDMSF: Inferring Associations Between Long Non-Coding RNA and Disease Based on Multi-Similarity Fusion.

IEEE/ACM transactions on computational biology and bioinformatics
The dysregulation and mutation of long non-coding RNAs (lncRNAs) have been proved to result in a variety of human diseases. Identifying potential disease-related lncRNAs may benefit disease diagnosis, treatment and prognosis. A number of methods have...

A Sentence-Level Joint Relation Classification Model Based on Reinforcement Learning.

Computational intelligence and neuroscience
Relation classification is an important semantic processing task in the field of natural language processing (NLP). Data sources generally adopt remote monitoring strategies to automatically generate large-scale training data, which inevitably causes...

An Examination of the Statistical Laws of Semantic Change in Clinical Notes.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Natural language is continually changing. Given the prevalence of unstructured, free-text clinical notes in the healthcare domain, understanding the aspects of this change is of critical importance to clinical Natural Language Processing (NLP) system...

Quantification of BERT Diagnosis Generalizability Across Medical Specialties Using Semantic Dataset Distance.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Deep learning models in healthcare may fail to generalize on data from unseen corpora. Additionally, no quantitative metric exists to tell how existing models will perform on new data. Previous studies demonstrated that NLP models of medical notes ge...

Soft and self constrained clustering for group-based labeling.

Medical image analysis
When using deep neural networks in medical image classification tasks, it is mandatory to prepare a large-scale labeled image set, and this often requires significant effort by medical experts. One strategy to reduce the labeling cost is group-based ...

Hybrid Pyramid Convolutional Network for Multiscale Face Detection.

Computational intelligence and neuroscience
Face detection remains a challenging problem due to the high variability of scale and occlusion despite the strong representational power of deep convolutional neural networks and their implicit robustness. To handle hard face detection under extreme...

Generative Adversarial Network with Multi-branch Discriminator for imbalanced cross-species image-to-image translation.

Neural networks : the official journal of the International Neural Network Society
There has been an increased interest in high-level image-to-image translation to achieve semantic matching. Through a powerful translation model, we can efficiently synthesize high-quality images with diverse appearances while retaining semantic matc...