AIMC Topic: Algorithms

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Understanding artificial intelligence based radiology studies: What is overfitting?

Clinical imaging
Artificial intelligence (AI) is a broad umbrella term used to encompass a wide variety of subfields dedicated to creating algorithms to perform tasks that mimic human intelligence. As AI development grows closer to clinical integration, radiologists ...

Development of automatic measurement for patellar height based on deep learning and knee radiographs.

European radiology
OBJECTIVES: To develop and evaluate the performance of a deep learning-based system for automatic patellar height measurements using knee radiographs.

Artificial intelligence for early prediction of pulmonary hypertension using electrocardiography.

The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation
BACKGROUND: Screening and early diagnosis of pulmonary hypertension (PH) are critical for managing progression and preventing associated mortality; however, there are no tools for this purpose. We developed and validated an artificial intelligence (A...

Graph Convolution Networks with manifold regularization for semi-supervised learning.

Neural networks : the official journal of the International Neural Network Society
In recent times, Graph Convolution Networks (GCN) have been proposed as a powerful tool for graph-based semi-supervised learning. In this paper, we introduce a model that enhances label propagation of Graph Convolution Networks (GCN). More precisely,...

Drug-drug interaction extraction via hybrid neural networks on biomedical literature.

Journal of biomedical informatics
Adverse events caused by drug-drug interaction (DDI) not only pose a serious threat to health, but also increase additional medical care expenditure. However, despite the emergence of many excellent text mining-based DDI classification methods, achie...

Analyzing the Effectiveness of the Brain-Computer Interface for Task Discerning Based on Machine Learning.

Sensors (Basel, Switzerland)
The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using ...

Facial Expressions Recognition for Human-Robot Interaction Using Deep Convolutional Neural Networks with Rectified Adam Optimizer.

Sensors (Basel, Switzerland)
The interaction between humans and an NAO robot using deep convolutional neural networks (CNN) is presented in this paper based on an innovative end-to-end pipeline method that applies two optimized CNNs, one for face recognition (FR) and another one...

Brain experiments imply adaptation mechanisms which outperform common AI learning algorithms.

Scientific reports
Attempting to imitate the brain's functionalities, researchers have bridged between neuroscience and artificial intelligence for decades; however, experimental neuroscience has not directly advanced the field of machine learning (ML). Here, using neu...

BioConceptVec: Creating and evaluating literature-based biomedical concept embeddings on a large scale.

PLoS computational biology
A massive number of biological entities, such as genes and mutations, are mentioned in the biomedical literature. The capturing of the semantic relatedness of biological entities is vital to many biological applications, such as protein-protein inter...

DeepDetectNet vs RLAttackNet: An adversarial method to improve deep learning-based static malware detection model.

PloS one
Deep learning methods are being increasingly widely used in static malware detection field because they can summarize the feature of malware and its variants that have never appeared before. But similar to the picture recognition model, the static ma...