AIMC Topic: Neural Networks, Computer

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Multi-task learning for quality assessment of fetal head ultrasound images.

Medical image analysis
It is essential to measure anatomical parameters in prenatal ultrasound images for the growth and development of the fetus, which is highly relied on obtaining a standard plane. However, the acquisition of a standard plane is, in turn, highly subject...

Abdominal artery segmentation method from CT volumes using fully convolutional neural network.

International journal of computer assisted radiology and surgery
PURPOSEĀ : The purpose of this paper is to present a fully automated abdominal artery segmentation method from a CT volume. Three-dimensional (3D) blood vessel structure information is important for diagnosis and treatment. Information about blood ves...

Physics-driven learning of x-ray skin dose distribution in interventional procedures.

Medical physics
PURPOSE: Radiation doses accumulated during very complicated image-guided x-ray procedures have the potential to cause stochastic, but also deterministic effects, such as skin rashes or even hair loss. To monitor and reduce radiation-related risks to...

Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation.

Journal of chemical information and modeling
We propose a novel deep learning approach for predicting drug-target interaction using a graph neural network. We introduce a distance-aware graph attention algorithm to differentiate various types of intermolecular interactions. Furthermore, we extr...

Multiple-level biomedical event trigger recognition with transfer learning.

BMC bioinformatics
BACKGROUND: Automatic extraction of biomedical events from literature is an important task in the understanding biological systems, allowing for faster update of the latest discoveries automatically. Detecting trigger words which indicate events is a...

Multi-Atlas Segmentation of Anatomical Brain Structures Using Hierarchical Hypergraph Learning.

IEEE transactions on neural networks and learning systems
Accurate segmentation of anatomical brain structures is crucial for many neuroimaging applications, e.g., early brain development studies and the study of imaging biomarkers of neurodegenerative diseases. Although multi-atlas segmentation (MAS) has a...

Effect of Lower Third Molar Segmentations on Automated Tooth Development Staging using a Convolutional Neural Network.

Journal of forensic sciences
Staging third molar development is commonly used for age estimation in subadults. Automated developmental stage allocation to the mandibular left third molar in panoramic radiographs has been examined in a pilot study. This method used an AlexNet Dee...

Forecasting of bioaerosol concentration by a Back Propagation neural network model.

The Science of the total environment
Bioaerosol in the atmosphere plays a very important role in environment and public health. To forecast the bioaerosol concentration, the correlation between bioaerosol concentration and meteorological factors was discussed, and a Back Propagation (BP...

Two-step deep neural network for segmentation of deep white matter hyperintensities in migraineurs.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Patients with migraine show an increased presence of white matter hyperintensities (WMHs), especially deep WMHs. Segmentation of small, deep WMHs is a critical issue in managing migraine care. Here, we aim to develop a novel...

Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks.

Medical image analysis
Accurate diagnosis of thyroid nodules using ultrasonography is a valuable but tough task even for experienced radiologists, considering both benign and malignant nodules have heterogeneous appearances. Computer-aided diagnosis (CAD) methods could pot...