AIMC Topic: Semantics

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Semantic-Rearrangement-based Hierarchical Alignment for domain generalized segmentation.

Neural networks : the official journal of the International Neural Network Society
Domain generalized semantic segmentation is an essential computer vision task, for which models only leverage source data to learn semantic segmentation towards generalizing to the unseen target domains. Previous works typically address this challeng...

Semantic Convergence with LLMs for Head and Neck Cancer Quality Indicators.

Studies in health technology and informatics
We developed a novel method for leveraging large language models (LLM) to systematically filter and categorize large numbers of clinical quality indicators (CQI) for head and neck cancer. This was used to transform a tedious, human-resource intensive...

Improving Radiology Report Generation with Semantic Understanding.

Studies in health technology and informatics
This study proposes RRG-LLM, a model designed to enhance RRG by effectively learning medical domain with minimal computational resources. Initially, LLM is finetuned by LoRA, enabling efficient adaptation to the medical domain. Subsequently, only the...

Multimodal Fusion of EHR in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and LLM.

Studies in health technology and informatics
In recent decades, Electronic Health Records (EHRs) have become increasingly useful to support clinical decision-making and healthcare. EHRs usually contain heterogeneous information, such as structural data in tabular form and un-structured data in ...

Empirical Antonym Implementation in the UMLS SPECIALIST Lexicon.

Studies in health technology and informatics
Antonyms are words that have opposite or contrasting meanings in a specific domain. For example, "increase" is the opposite of "decrease" in the domain of "quantity". Antonyms play an important role in NLP applications to improve performance. This pa...

Toward a Universal Map of EEG: A Semantic, Low-Dimensional Manifold for EEG Classification, Clustering, and Prognostication.

Annals of neurology
OBJECTIVE: Prognostication in patients with disorders of consciousness (DOCs) remains challenging because of heterogeneous etiologies, pathophysiologies and, consequently, highly variable electroencephalograms (EEGs). Here, we use EEG patterns that a...

Entity replacement strategy for temporal knowledge graph query relaxation.

Neural networks : the official journal of the International Neural Network Society
The temporal knowledge graph (TKG) query enables the retrieval of candidate answer lists by addressing questions that involve temporal constraints, regarded as a crucial downstream task in the realm of the temporal knowledge graph. Existing methods p...

Could vehicles analyze driving risks using human fuzzy semantic logic? A data-knowledge-driven new perspective.

Accident; analysis and prevention
Accurate risk identification is crucial for ensuring the safe operation of Host vehicles (HoVs) in environments shared with Neighboring vehicles (NeVs). Traditional risk identification mechanisms typically rely on large amounts of precise numerical d...

LitSense 2.0: AI-powered biomedical information retrieval with sentence and passage level knowledge discovery.

Nucleic acids research
LitSense 2.0 (https://www.ncbi.nlm.nih.gov/research/litsense2/) is an advanced biomedical search system enhanced with dense vector semantic retrieval, designed for accessing literature on sentence and paragraph levels. It provides unified access to 3...

CAS: enhancing implicit constrained data augmentation with semantic enrichment for biomedical relation extraction and beyond.

Database : the journal of biological databases and curation
Biomedical relation extraction often involves datasets with implicit constraints, where structural, syntactic, or semantic rules must be strictly preserved to maintain data integrity. Traditional data augmentation techniques struggle in these scenari...