AIMC Topic: Semantics

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Multi-scale fusion semantic enhancement network for medical image segmentation.

Scientific reports
The application of sophisticated computer vision techniques for medical image segmentation (MIS) plays a vital role in clinical diagnosis and treatment. Although Transformer-based models are effective at capturing global context, they are often ineff...

Multiclass semantic segmentation for prime disease detection with severity level identification in Citrus plant leaves.

Scientific reports
Agriculture provides the basics for producing food, driving economic growth, and maintaining environmental sustainability. On the other hand, plant diseases have the potential to reduce crop productivity and raise expenses, posing a risk to food secu...

Exploring semantic grounding in the posterior parietal cortex.

Brain structure & function
This study examines the evolving perspective on semantic processing, which has shifted from the traditional view of an isolated semantic memory system to one that recognizes the involvement of dynamic, distributed neural networks. Recent evidence sup...

Edge computing based english translation model using fuzzy semantic optimal control technique.

PloS one
People's need for English translation is gradually growing in the modern era of technological advancements, and a computer that can comprehend and interpret English is now more crucial than ever. Some issues, including ambiguity in English translatio...

CTFS: A consolidated transformer framework for instance and semantic segmentation tasks.

Neural networks : the official journal of the International Neural Network Society
Instance segmentation and semantic segmentation are fundamental tasks that support many computer vision applications. Recently, researchers have investigated the feasibility of constructing a unified transformer framework and leveraging multi-task le...

Fine-grained image generation with EEG multi-level semantics.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Decoding visual information from electroencephalography (EEG) signals is crucial in neuroscience and artificial intelligence. While existing methods have been able to extract high-level features such as object categories, th...

Comprehensive disentanglement with fine-grained feature mitigation for domain generalization.

Neural networks : the official journal of the International Neural Network Society
Domain generalization is proposed as an approach capable of solving the domain shift challenge, which aims at generalizing knowledge learned from multiple source domains with different distributions to the target domain that is invisible during the t...

Unified semantic space learning for cross-modal retrieval.

Neural networks : the official journal of the International Neural Network Society
With the increasing amount of multimodal data on the Internet, cross-modal retrieval has gradually become a hot research topic and has achieved significant progress, especially since graph convolutional networks were introduced. Most methods based on...

Zero- and few-shot Named Entity Recognition and Text Expansion in medication prescriptions using large language models.

Artificial intelligence in medicine
Medication prescriptions in electronic health records (EHR) are often in free-text and may include a mix of languages, local brand names, and a wide range of idiosyncratic formats and abbreviations. Large language models (LLMs) have shown a promising...

A comparative study of recent large language models on generating hospital discharge summaries for lung cancer patients.

Journal of biomedical informatics
OBJECTIVE: Generating discharge summaries is a crucial yet time-consuming task in clinical practice, essential for conveying pertinent patient information and facilitating continuity of care. Recent advancements in large language models (LLMs) have s...