AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Semantics

Showing 141 to 150 of 1350 articles

Clear Filters

Integration of multi-level semantics in PTMs with an attention model for question matching.

PloS one
The task of question matching/retrieval focuses on determining whether two questions are semantically equivalent. It has garnered significant attention in the field of natural language processing (NLP) due to its commercial value. While neural networ...

Interactive dual-stream contrastive learning for radiology report generation.

Journal of biomedical informatics
Radiology report generation automates diagnostic narrative synthesis from medical imaging data. Current report generation methods primarily employ knowledge graphs for image enhancement, neglecting the interpretability and guiding function of the kno...

CMCN: Chinese medical concept normalization using continual learning and knowledge-enhanced.

Artificial intelligence in medicine
Medical Concept Normalization (MCN) is a crucial process for deep information extraction and natural language processing tasks, which plays a vital role in biomedical research. Although MCN in English has achieved significant research achievements, C...

Reconstruct incomplete relation for incomplete modality brain tumor segmentation.

Neural networks : the official journal of the International Neural Network Society
Different brain tumor magnetic resonance imaging (MRI) modalities provide diverse tumor-specific information. Previous works have enhanced brain tumor segmentation performance by integrating multiple MRI modalities. However, multi-modal MRI data are ...

Strong and weak alignment of large language models with human values.

Scientific reports
Minimizing negative impacts of Artificial Intelligent (AI) systems on human societies without human supervision requires them to be able to align with human values. However, most current work only addresses this issue from a technical point of view, ...

Benchmarking robustness of deep neural networks in semantic segmentation of fluorescence microscopy images.

BMC bioinformatics
BACKGROUND: Fluorescence microscopy (FM) is an important and widely adopted biological imaging technique. Segmentation is often the first step in quantitative analysis of FM images. Deep neural networks (DNNs) have become the state-of-the-art tools f...

Designing an effective semantic fluency test for early MCI diagnosis with machine learning.

Computers in biology and medicine
Semantic fluency tests are one of the key tests used in batteries for the early detection of Mild Cognitive Impairment (MCI) as the impairment in speech and semantic memory are among the first symptoms, attracting the attention of a large number of s...

Semi-Supervised Semantic Image Segmentation by Deep Diffusion Models and Generative Adversarial Networks.

International journal of neural systems
Typically, deep learning models for image segmentation tasks are trained using large datasets of images annotated at the pixel level, which can be expensive and highly time-consuming. A way to reduce the amount of annotated images required for traini...

CASL: Capturing Activity Semantics Through Location Information for Enhanced Activity Recognition.

IEEE/ACM transactions on computational biology and bioinformatics
Using portable tools to monitor and identify daily activities has increasingly become a focus of digital healthcare, especially for elderly care. One of the difficulties in this area is the excessive reliance on labeled activity data for correspondin...

ICDXML: enhancing ICD coding with probabilistic label trees and dynamic semantic representations.

Scientific reports
Accurately assigning standardized diagnosis and procedure codes from clinical text is crucial for healthcare applications. However, this remains challenging due to the complexity of medical language. This paper proposes a novel model that incorporate...