AIMC Topic: Deep Learning

Clear Filters Showing 2941 to 2950 of 28423 articles

DRExplainer: Quantifiable interpretability in drug response prediction with directed graph convolutional network.

Artificial intelligence in medicine
Predicting the response of a cancer cell line to a therapeutic drug is pivotal for personalized medicine. Despite numerous deep learning methods that have been developed for drug response prediction, integrating diverse information about biological e...

Novel augmentation techniques using diffusion models for green wall plant health classification.

Computers in biology and medicine
Green walls, vertical plant-based structures, are increasingly popular due to their diverse environmental benefits, including aesthetic enhancement, temperature and humidity regulation, and air pollutant removal. These systems, typically consisting o...

Modeling crash avoidance behaviors in vehicle-pedestrian near-miss scenarios: Curvilinear time-to-collision and Mamba-driven deep reinforcement learning.

Accident; analysis and prevention
Interactions between vehicle-pedestrian at intersections often lead to safety-critical situations. This study aims to model the crash avoidance behaviors of vehicles during interactions with pedestrians in near-miss scenarios, contributing to the dev...

AD-VAE: Adversarial Disentangling Variational Autoencoder.

Sensors (Basel, Switzerland)
Face recognition (FR) is a less intrusive biometrics technology with various applications, such as security, surveillance, and access control systems. FR remains challenging, especially when there is only a single image per person as a gallery datase...

Deep Learning-Emerged Grid Cells-Based Bio-Inspired Navigation in Robotics.

Sensors (Basel, Switzerland)
Grid cells in the brain's entorhinal cortex are essential for spatial navigation and have inspired advancements in robotic navigation systems. This paper first provides an overview of recent research on grid cell-based navigation in robotics, focusin...

Performance Improvement of a Natural Language Processing Tool for Extracting Patient Narratives Related to Medical States From Japanese Pharmaceutical Care Records by Increasing the Amount of Training Data: Natural Language Processing Analysis and Validation Study.

JMIR medical informatics
BACKGROUND: Patients' oral expressions serve as valuable sources of clinical information to improve pharmacotherapy. Natural language processing (NLP) is a useful approach for analyzing unstructured text data, such as patient narratives. However, few...

A deep learning model for radiological measurement of adolescent idiopathic scoliosis using biplanar radiographs.

Journal of orthopaedic surgery and research
BACKGROUND: Accurate measurement of the spinal alignment parameters is crucial for diagnosing and evaluating adolescent idiopathic scoliosis (AIS). Manual measurement is subjective and time-consuming. The recently developed artificial intelligence mo...

Development of model for identifying homologous recombination deficiency (HRD) status of ovarian cancer with deep learning on whole slide images.

Journal of translational medicine
BACKGROUND: Homologous recombination deficiency (HRD) refers to the dysfunction of homologous recombination repair (HRR) at the cellular level. The assessment of HRD status has the important significance for the formulation of treatment plans, effica...

Automated classification of chest X-rays: a deep learning approach with attention mechanisms.

BMC medical imaging
BACKGROUND: Pulmonary diseases such as COVID-19 and pneumonia, are life-threatening conditions, that require prompt and accurate diagnosis for effective treatment. Chest X-ray (CXR) has become the most common alternative method for detecting pulmonar...

Evolution of AI enabled healthcare systems using textual data with a pretrained BERT deep learning model.

Scientific reports
In the rapidly evolving field of healthcare, Artificial Intelligence (AI) is increasingly driving the promotion of the transformation of traditional healthcare and improving medical diagnostic decisions. The overall goal is to uncover emerging trends...