AIMC Topic: Neural Networks, Computer

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Chinese clinical named entity recognition via multi-head self-attention based BiLSTM-CRF.

Artificial intelligence in medicine
Clinical named entity recognition (CNER) is a fundamental step for many clinical Natural Language Processing (NLP) systems, which aims to recognize and classify clinical entities such as diseases, symptoms, exams, body parts and treatments in clinica...

Instance importance-Aware graph convolutional network for 3D medical diagnosis.

Medical image analysis
Automatic diagnosis of 3D medical data is a significant goal of intelligent healthcare. By exploiting the abundant pathological information of 3D data, human experts and algorithms can provide accurate predictions for patients. Considering the high c...

Autonomous diagnosis of pediatric cutaneous vascular anomalies using a convolutional neural network.

International journal of pediatric otorhinolaryngology
OBJECTIVES: Design and validate a novel handheld device for the autonomous diagnosis of pediatric vascular anomalies using a convolutional neural network (CNN).

Predicting residues involved in anti-DNA autoantibodies with limited neural networks.

Medical & biological engineering & computing
Computer-aided rational vaccine design (RVD) and synthetic pharmacology are rapidly developing fields that leverage existing datasets for developing compounds of interest. Computational proteomics utilizes algorithms and models to probe proteins for ...

SDFormer: A Novel Transformer Neural Network for Structural Damage Identification by Segmenting the Strain Field Map.

Sensors (Basel, Switzerland)
Damage identification is a key problem in the field of structural health monitoring, which is of great significance to improve the reliability and safety of engineering structures. In the past, the structural strain damage identification method based...

Using deep learning to predict human decisions and using cognitive models to explain deep learning models.

Scientific reports
Deep neural networks (DNNs) models have the potential to provide new insights in the study of cognitive processes, such as human decision making, due to their high capacity and data-driven design. While these models may be able to go beyond theory-dr...

Brain tumor classification of magnetic resonance images using a novel CNN-based medical image analysis and detection network in comparison with AlexNet.

Journal of population therapeutics and clinical pharmacology = Journal de la therapeutique des populations et de la pharmacologie clinique
AIM: This research work aims at developing an automatic medical image analysis and detection for accurate classification of brain tumors from a magnetic resonance imaging (MRI) dataset. We developed a new MIDNet18 CNN architecture in comparison with ...

Evaluation and Monitoring of Endometrial Cancer Based on Magnetic Resonance Imaging Features of Deep Learning.

Contrast media & molecular imaging
This study was aimed to compare and analyze the magnetic resonance imaging (MRI) manifestations and surgical pathological results of endometrial cancer (EC) and to explore the clinical research of MRI in the diagnosis and staging of EC. . 80 patients...

Problems and Countermeasures of Financial Risk in Project Management Based on Convolutional Neural Network.

Computational intelligence and neuroscience
Under the background of market economy, engineering projects are faced with a lot of financial risks. If we cannot prevent them effectively, it will undoubtedly bring serious negative impact to the entire engineering management work. Therefore, it is...

Prediction Model of Stress Intensity Factor of Circumferential Through Crack in Elbow Based on Neural Network.

Computational intelligence and neuroscience
Using ANSYS software to establish the finite element model of crack bending tube, the SIF at the tip of the crack is calculated for the difference in the diameter of the pipe, the outer diameter of the elbow, and the bending angle of the bend pipe, a...