AIMC Topic: Semantics

Clear Filters Showing 1181 to 1190 of 1465 articles

A medical information extraction model with contrastive tuning and tagging layer training.

Computers in biology and medicine
Medical information extraction, as a core task in medical intelligent systems, focuses on extracting necessary structured information from clinical texts. In recent years, deep learning-based methods have become mainstream and often achieve superior ...

Weakly-supervised semantic segmentation in histology images using contrastive learning and self-training.

Computers in biology and medicine
This paper presents a novel method for weakly-supervised semantic segmentation (WSSS) of histology images, where only global image-level labels are employed. We leverage an existing weakly-supervised object localization (WSOL) method to generate clas...

MSPDD-net: Mamba semantic perception dual decoding network for retinal image vessel segmentation.

Computers in biology and medicine
In the Retinal Image Vessel (RIV) segmentation task, due to existing a large number of low-contrast capillaries in the image usually leads to the problem of poor segmentation accuracy. To address this issue, this study aims to fully model the global ...

Biomedical text normalization through generative modeling.

Journal of biomedical informatics
OBJECTIVE: A large proportion of electronic health record (EHR) data consists of unstructured medical language text. The formatting of this text is often flexible and inconsistent, making it challenging to use for predictive modeling, clinical decisi...

BERTAgent: The development of a novel tool to quantify agency in textual data.

Journal of experimental psychology. General
Pertaining to goal orientation and achievement, agency is a fundamental aspect of human cognition and behavior. Accordingly, detecting and quantifying linguistic encoding of agency are critical for the analysis of human actions, interactions, and soc...

A semantic enhancement-based multimodal network model for extracting information from evidence lists.

Neural networks : the official journal of the International Neural Network Society
Courts require the extraction of crucial information about various cases from heterogeneous evidence lists for knowledge-driven decision-making. However, traditional manual screening is complex and inaccurate when confronted with massive evidence lis...

A shape composition method for named entity recognition.

Neural networks : the official journal of the International Neural Network Society
Large language models (LLMs) roughly encode a sentence into a dense representation (a vector), which mixes up the semantic expression of all named entities within a sentence. So the decoding process is easily overwhelmed by sentence-specific informat...

Unambiguous granularity distillation for asymmetric image retrieval.

Neural networks : the official journal of the International Neural Network Society
Previous asymmetric image retrieval methods based on knowledge distillation have primarily focused on aligning the global features of two networks to transfer global semantic information from the gallery network to the query network. However, these m...

Multi-level semantic-aware transformer for image captioning.

Neural networks : the official journal of the International Neural Network Society
Effective visual representation is crucial for image captioning task. Among the existing methods, the grid-based visual encoding methods take fragmented features extracted from the entire image as input, lacking the fine-grained semantic information ...

Adaptive decoupling-fusion in Siamese network for image classification.

Neural networks : the official journal of the International Neural Network Society
Convolutional neural networks (CNNs) are highly regarded for their ability to extract semantic information from visual inputs. However, this capability often leads to the inadvertent loss of important visual details. In this paper, we introduce an Ad...