AIMC Topic: Neural Networks, Computer

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Improving prediction for medical institution with limited patient data: Leveraging hospital-specific data based on multicenter collaborative research network.

Artificial intelligence in medicine
BACKGROUND AND OBJECTIVE: Clinical decision support assisted by prediction models usually faces the challenges of limited clinical data and a lack of labels when the model is developed with data from a single medical institution. Accordingly, researc...

Liver segmentation in abdominal CT images via auto-context neural network and self-supervised contour attention.

Artificial intelligence in medicine
OBJECTIVE: Accurate image segmentation of the liver is a challenging problem owing to its large shape variability and unclear boundaries. Although the applications of fully convolutional neural networks (CNNs) have shown groundbreaking results, limit...

Association of AI quantified COVID-19 chest CT and patient outcome.

International journal of computer assisted radiology and surgery
PURPOSE: Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to d...

Cardiac Severity Classification Using Pre Trained Neural Networks.

Interdisciplinary sciences, computational life sciences
Electrocardiogram (ECG) is the most effective instrument for making decisions about various forms of heart disease. As a result, several researchers have focused on the ECG signal to extract the features of heartbeats with high precision and efficien...

Artificial intelligence for detection of periapical lesions on intraoral radiographs: Comparison between convolutional neural networks and human observers.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: The aim of this study was to compare the diagnostic performance of convolutional neural networks (CNNs) with the performance of human observers for the detection of simulated periapical lesions on periapical radiographs.

Towards evaluating the robustness of deep diagnostic models by adversarial attack.

Medical image analysis
Deep learning models (with neural networks) have been widely used in challenging tasks such as computer-aided disease diagnosis based on medical images. Recent studies have shown deep diagnostic models may not be robust in the inference process and m...

Artificial Intelligence in Nutrients Science Research: A Review.

Nutrients
Artificial intelligence (AI) as a branch of computer science, the purpose of which is to imitate thought processes, learning abilities and knowledge management, finds more and more applications in experimental and clinical medicine. In recent decades...

Central Attention and a Dual Path Convolutional Neural Network in Real-World Tree Species Recognition.

International journal of environmental research and public health
Identifying plants is not only the job of professionals, but also useful or essential for the plant lover and the general public. Although deep learning approaches for plant recognition are promising, driven by the success of convolutional neural net...

A stack LSTM structure for decoding continuous force from local field potential signal of primary motor cortex (M1).

BMC bioinformatics
BACKGROUND: Brain Computer Interfaces (BCIs) translate the activity of the nervous system to a control signal which is interpretable for an external device. Using continuous motor BCIs, the user will be able to control a robotic arm or a disabled lim...

Medical Image Analysis Using AM-FM Models and Methods.

IEEE reviews in biomedical engineering
Medical image analysis methods require the use of effective representations for differentiating between lesions, diseased regions, and normal structure. Amplitude Modulation-Frequency Modulation (AM-FM) models provide effective representations throug...