AIMC Topic: Algorithms

Clear Filters Showing 361 to 370 of 28713 articles

Improve deep learning-based reconstruction of optical coherence tomography angiography by siamese U-Net.

Biomedical physics & engineering express
Optical coherence tomography angiography (OCTA), as a functional imaging based on OCT, has found successful medical applications. OCTA produces vasculature imaging using blood flow motion as an intrinsic contrast agent. To date, the prevailing OCTA a...

Classification of cardiac electrical signals between patients with myocardial infarction and healthy controls by using time-frequency features and 3D convolutional neural networks.

Biomedical physics & engineering express
Electrocardiogram (ECG) signal classification plays an important role in myocardial infarction (MI) detection and screening. Despite that much progress has been made, the interpretation of ECG signals is still extremely time-consuming, and heavily re...

Torso synthetic CT generation by integrating deep learning and segmentation for FDG-PET/MR attenuation correction.

Biomedical physics & engineering express
Positron Emission Tomography/Magnetic Resonance () offers benefits over PET/CT including simultaneous PET and MR acquisition, intrinsic spatial registration accuracy, MR-based functional information, and superior soft tissue contrast. However, accura...

CSCST-Net: a fully sparse-regularized convolutional sparse coding network for low-dose CT denoising.

Biomedical physics & engineering express
. Most low-dose computed tomography (LDCT) denoising methods based on CNN have some denoising effect, but their interpretability is very low due to the black-box nature of neural networks.. To address this issue, we propose a novel fully sparse-regul...

TXSelect: A multi-task learning model to identify secretory effectors.

PLoS computational biology
Secretory effectors from pathogenic microorganisms significantly influence pathogen survival and pathogenicity by manipulating host signalling, immune responses, and metabolic processes. However, because of sequence and structural heterogeneity among...

Machine learning based fault classification for improved induction motor performance.

PloS one
This study explores the design of an effective fault classification algorithm for 3 phase induction motor, an integral unit in many industrial systems. It is found that traditional fault detection methods and deep learning approaches are both effecti...

Evaluation of model performance in predicting sepsis after intestinal obstruction surgery: a multicenter retrospective study.

Annals of medicine
PURPOSE: Intestinal obstruction surgery is a high-risk procedure associated with postoperative sepsis. In this multicenter retrospective study, we aimed to employ machine-learning methods to predict sepsis after intestinal obstruction surgery and vis...

MaskGraphene: an advanced framework for interpretable joint representation for multi-slice, multi-condition spatial transcriptomics.

Genome biology
Recent advances in spatial transcriptomics (ST) highlight the need to integrate multiple slices for joint analysis. A key challenge is generating interpretable embeddings that preserve spatial geometry while correcting batch effects. We present MaskG...

Detection of commercial crop weeds using machine learning algorithms.

Scientific reports
This work investigates the YOLOv5 object detection algorithms for classifying commercial crops such as tomatoes, chili, and cotton. The data sets comprise 707 images of green chillies, 200 images of tomato crops and 130 images of weeds from Ponnandag...

Introducing FREM: a decision-support approach for automated identification of individuals at high imminent fracture risk.

Archives of osteoporosis
UNLABELLED: This study used explainable AI to improve the Danish FREM model for predicting one-year risk of major osteoporotic fractures in over 2.4 million individuals aged ≥ 45. A DART boosting algorithm improved performance (AUC 0.77), with explai...