AIMC Topic: Deep Learning

Clear Filters Showing 451 to 460 of 28423 articles

Multimodal pathomics and clinical features predict postresection permanent hydrocephalus in pediatric medulloblastoma.

Journal of neuro-oncology
PURPOSE: Predicting postoperative persistent hydrocephalus risk in pediatric medulloblastoma remains challenging using conventional clinical features. We investigated whether deep learning (DL) of pathomic features could improve postoperative hydroce...

A review of the application of deep learning in thyroid nodule imaging: from model architectures to training methods and core image analysis tasks.

Biomedical physics & engineering express
Thyroid nodules are highly prevalent in clinical practice, and their incidence has been steadily increasing in recent years, posing significant threats to human health. Traditional imaging examinations for thyroid nodules rely heavily on physicians' ...

Automated forest land division using deep learning and drone imagery.

PloS one
This paper proposes an automated solution for tree enumeration in areas designated for forest land division using drone image processing. Traditional tree counting methods are time-consuming and error-prone. Our approach leverages drone imagery and a...

Contrast limited adaptive histogram equalization (CLAHE) and colour difference histogram (CDH) feature merging capsule network (CCFMCapsNet) for complex image recognition.

PloS one
To enhance crop yield, detecting leaf diseases has become a crucial research focus. Deep learning and computer vision excel in digital image processing. Various techniques grounded in deep learning have been utilized for detecting plant leaf diseases...

The group-housed pigs attacking and daily behaviors detection and tracking based on improved YOLOv5s and DeepSORT.

PloS one
Automatic detection and tracking of pig behaviors through video surveillance remain challenges due to farm demanding conditions, e.g., illumination conditions and occlusion of one pig from another. The main goal of this study is to develop a deep lea...

Serum-MiR-CanPred: deep learning framework for pan-cancer classification and miRNA-targeted drug discovery.

RNA biology
Cancer diagnosis at an early stage is crucial for improving overall health outcomes. However, existing cancer diagnostic techniques are mostly invasive and tend to identify the disease only in its advanced stages. MicroRNAs (miRNAs), which are small ...

Durative Monitoring of Sulfur Hexafluoride Characteristic Gases under Hydrogen Interference Using a Time2Vec-Encoded CNN-Transformer-LSTM Model Based on a Heterogeneous Gas Sensor Array.

ACS sensors
Gas-insulated switchgear (GIS) systems extensively employ sulfur hexafluoride (SF) as an insulating medium and are widely deployed in modern power systems. Under partial discharge (PD) conditions, SF decomposes to generate hazardous byproducts such a...

Artificial intelligence in cancer: applications, challenges, and future perspectives.

Molecular cancer
Artificial intelligence (AI) is rapidly revolutionizing the landscape of oncological research and the advancement of personalized clinical interventions. Progress in three interconnected areas, including the development of methods and algorithms for ...

Assessment of a Grad-CAM interpretable deep learning model for HAPE diagnosis: performance and pitfalls in severity stratification from chest radiographs.

BMC medical informatics and decision making
OBJECTIVES: To investigate the feasibility of a deep learning model, using a transfer learning approach, for recognizing high-altitude pulmonary edema (HAPE) on chest X-ray images and exploring its capability for assessing severity.

MDG-DDI: multi-feature drug graph for drug-drug interaction prediction.

BMC bioinformatics
BACKGROUND: Drug-drug interactions (DDIs) frequently occur in combination therapy and may cause adverse effects or reduced efficacy. Existing computational approaches often fail to capture both the semantic information in drug sequences and the struc...