AIMC Topic: Neural Networks, Computer

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Deep neural models for automated multi-task diagnostic scan management-quality enhancement, view classification and report generation.

Biomedical physics & engineering express
The detailed physiological perspectives captured by medical imaging provides actionable insights to doctors to manage comprehensive care of patients. However, the quality of such diagnostic image modalities is often affected by mismanagement of the i...

Prediction of the Least Principal Stresses Using Drilling Data: A Machine Learning Application.

Computational intelligence and neuroscience
The least principal stresses of downhole formations include minimum horizontal stress ( ) and maximum horizontal stress ( ). and are substantial parameters that significantly affect the design and optimization of the drilling process. These stres...

Research on Digital Economy of Intelligent Emergency Risk Avoidance in Sudden Financial Disasters Based on PSO-BPNN Algorithm.

Computational intelligence and neuroscience
In recent years, disasters have seriously affected the normal development of financial business in some regions. At the time of disaster, how to effectively integrate resources of all parties, deal with sudden financial disasters efficiently, and res...

Deep neural network for video colonoscopy of ulcerative colitis: a cross-sectional study.

The lancet. Gastroenterology & hepatology
BACKGROUND: A combination of endoscopic and histological evaluation is important in the management of patients with ulcerative colitis. We aimed to adapt our previous deep neural network system (deep neural ulcerative colitis [DNUC]) to full video co...

Deep learning for abdominal adipose tissue segmentation with few labelled samples.

International journal of computer assisted radiology and surgery
PURPOSE: Fully automated abdominal adipose tissue segmentation from computed tomography (CT) scans plays an important role in biomedical diagnoses and prognoses. However, to identify and segment subcutaneous adipose tissue (SAT) and visceral adipose ...

DCACNet: Dual context aggregation and attention-guided cross deconvolution network for medical image segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Segmentation is a key step in biomedical image analysis tasks. Recently, convolutional neural networks (CNNs) have been increasingly applied in the field of medical image processing; however, standard models still have some ...

Deep learning-based ultrasonic dynamic video detection and segmentation of thyroid gland and its surrounding cervical soft tissues.

Medical physics
BACKGROUND: The prevalence of thyroid diseases has been increasing year by year. In this study, we established and validated a deep learning method (Cascade region-based convolutional neural network, R-CNN) based on ultrasound videos for automatic de...

Using k-mer embeddings learned from a Skip-gram based neural network for building a cross-species DNA N6-methyladenine site prediction model.

Plant molecular biology
This study used k-mer embeddings as effective feature to identify DNA N6-Methyladenine sites in plant genomes and obtained improved performance without substantial effort in feature extraction, combination and selection. Identification of DNA N6-meth...

Generative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification.

Sensors (Basel, Switzerland)
Generative adversarial networks (GANs) have been recently applied to medical imaging on different modalities (MRI, CT, X-ray, etc). However there are not many applications on ultrasound modality as a data augmentation technique applied to downstream ...

Vehicle Trajectory Estimation Based on Fusion of Visual Motion Features and Deep Learning.

Sensors (Basel, Switzerland)
Driver situation awareness is critical for safety. In this paper, we propose a fast, accurate method for obtaining real-time situation awareness using a single type of sensor: monocular cameras. The system tracks the host vehicle's trajectory using s...