AIMC Topic: Algorithms

Clear Filters Showing 9731 to 9740 of 28713 articles

Fully Convolutional Change Detection Framework With Generative Adversarial Network for Unsupervised, Weakly Supervised and Regional Supervised Change Detection.

IEEE transactions on pattern analysis and machine intelligence
Deep learning for change detection is one of the current hot topics in the field of remote sensing. However, most end-to-end networks are proposed for supervised change detection, and unsupervised change detection models depend on traditional pre-det...

Visible and Infrared Image Fusion Using Deep Learning.

IEEE transactions on pattern analysis and machine intelligence
Visible and infrared image fusion (VIF) has attracted a lot of interest in recent years due to its application in many tasks, such as object detection, object tracking, scene segmentation, and crowd counting. In addition to conventional VIF methods, ...

Optimizing Two-Way Partial AUC With an End-to-End Framework.

IEEE transactions on pattern analysis and machine intelligence
The Area Under the ROC Curve (AUC) is a crucial metric for machine learning, which evaluates the average performance over all possible True Positive Rates (TPRs) and False Positive Rates (FPRs). Based on the knowledge that a skillful classifier shoul...

Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection From Point Clouds.

IEEE transactions on pattern analysis and machine intelligence
Previous works for LiDAR-based 3D object detection mainly focus on the single-frame paradigm. In this paper, we propose to detect 3D objects by exploiting temporal information in multiple frames, i.e., point cloud videos. We empirically categorize th...

Two-Stage Self-Supervised Cycle-Consistency Transformer Network for Reducing Slice Gap in MR Images.

IEEE journal of biomedical and health informatics
Magnetic resonance (MR) images are usually acquired with large slice gap in clinical practice, i.e., low resolution (LR) along the through-plane direction. It is feasible to reduce the slice gap and reconstruct high-resolution (HR) images with the de...

System Based on Artificial Intelligence Edge Computing for Detecting Bedside Falls and Sleep Posture.

IEEE journal of biomedical and health informatics
Bedside falls and pressure ulcers are crucial issues in geriatric care. Although many bedside monitoring systems have been proposed, they are limited by the computational complexity of their algorithms. Moreover, most of the data collected by the sen...

Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge.

IEEE journal of biomedical and health informatics
In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of th...

Supervised learning and model analysis with compositional data.

PLoS computational biology
Supervised learning, such as regression and classification, is an essential tool for analyzing modern high-throughput sequencing data, for example in microbiome research. However, due to the compositionality and sparsity, existing techniques are ofte...

HCformer: Hybrid CNN-Transformer for LDCT Image Denoising.

Journal of digital imaging
Low-dose computed tomography (LDCT) is an effective way to reduce radiation exposure for patients. However, it will increase the noise of reconstructed CT images and affect the precision of clinical diagnosis. The majority of the current deep learnin...

ADis-QSAR: a machine learning model based on biological activity differences of compounds.

Journal of computer-aided molecular design
Drug candidates identified by the pharmaceutical industry typically have unique structural characteristics to ensure they interact strongly and specifically with their biological targets. Identifying these characteristics is a key challenge for devel...