AIMC Topic: Neural Networks, Computer

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Detection of maxillary sinus fungal ball via 3-D CNN-based artificial intelligence: Fully automated system and clinical validation.

PloS one
BACKGROUND: This study aims to develop artificial intelligence (AI) system to automatically classify patients with maxillary sinus fungal ball (MFB), chronic rhinosinusitis (CRS), and healthy controls (HCs).

Quantitative Analysis of DCE and DSC-MRI: From Kinetic Modeling to Deep Learning.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
BACKGROUND: Perfusion MRI is a well-established imaging modality with a multitude of applications in oncological and cardiovascular imaging. Clinically used processing methods, while stable and robust, have remained largely unchanged in recent years....

DeepFusion: A deep learning based multi-scale feature fusion method for predicting drug-target interactions.

Methods (San Diego, Calif.)
Predicting drug-target interactions (DTIs) is essential for both drug discovery and drug repositioning. Recently, deep learning methods have achieved relatively significant performance in predicting DTIs. Generally, it needs a large amount of approve...

Use of deep learning in the MRI diagnosis of Chiari malformation type I.

Neuroradiology
PURPOSE: To train deep learning convolutional neural network (CNN) models for classification of clinically significant Chiari malformation type I (CM1) on MRI to assist clinicians in diagnosis and decision making.

A regularization method to improve adversarial robustness of neural networks for ECG signal classification.

Computers in biology and medicine
With the advancement of machine leaning technologies, Deep Neural Networks (DNNs) have been utilized for automated interpretation of Electrocardiogram (ECG) signals to identify potential abnormalities in a patient's heart within a second. Studies hav...

Complex machine learning model needs complex testing: Examining predictability of molecular binding affinity by a graph neural network.

Journal of computational chemistry
Drug discovery pipelines typically involve high-throughput screening of large amounts of compounds in a search of potential drugs candidates. As a chemical space of small organic molecules is huge, a "navigation" over it urges for fast and lightweigh...

A multi-modal fusion framework based on multi-task correlation learning for cancer prognosis prediction.

Artificial intelligence in medicine
Morphological attributes from histopathological images and molecular profiles from genomic data are important information to drive diagnosis, prognosis, and therapy of cancers. By integrating these heterogeneous but complementary data, many multi-mod...

Structure-Aware Multimodal Deep Learning for Drug-Protein Interaction Prediction.

Journal of chemical information and modeling
Identifying drug-protein interactions (DPIs) is crucial in drug discovery, and a number of machine learning methods have been developed to predict DPIs. Existing methods usually use unrealistic data sets with hidden bias, which will limit the accurac...

Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques.

Sensors (Basel, Switzerland)
Diabetic Retinopathy (DR) is a predominant cause of visual impairment and loss. Approximately 285 million worldwide population is affected with diabetes, and one-third of these patients have symptoms of DR. Specifically, it tends to affect the patien...

Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach.

Sensors (Basel, Switzerland)
Extensive possibilities of applications have rendered emotion recognition ineluctable and challenging in the fields of computer science as well as in human-machine interaction and affective computing. Fields that, in turn, are increasingly requiring ...