AIMC Topic: Neural Networks, Computer

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Lung cancer survival period prediction and understanding: Deep learning approaches.

International journal of medical informatics
INTRODUCTION: Survival period prediction through early diagnosis of cancer has many benefits. It allows both patients and caregivers to plan resources, time and intensity of care to provide the best possible treatment path for the patients. In this p...

Smooth dendrite morphological neurons.

Neural networks : the official journal of the International Neural Network Society
A typical feature of hyperbox-based dendrite morphological neurons (DMN) is the generation of sharp and rough decision boundaries that inaccurately track the distribution shape of classes of patterns. This feature is because the minimum and maximum a...

Quantization Friendly MobileNet (QF-MobileNet) Architecture for Vision Based Applications on Embedded Platforms.

Neural networks : the official journal of the International Neural Network Society
Deep Neural Networks (DNNs) have become popular for various applications in the domain of image and computer vision due to their well-established performance attributes. DNN algorithms involve powerful multilevel feature extractions resulting in an e...

A Head-to Head Comparison of Machine Learning Algorithms for Identification of Implanted Cardiac Devices.

The American journal of cardiology
Application of artificial intelligence techniques in medicine has rapidly expanded in recent years. Two algorithms for identification of cardiac implantable electronic devices using chest radiography were recently developed: The PacemakerID algorithm...

A protein folding robot driven by a self-taught agent.

Bio Systems
This paper presents a computer simulation of a virtual robot that behaves as a peptide chain of the Hemagglutinin-Esterase protein (HEs) from human coronavirus. The robot can learn efficient protein folding policies by itself and then use them to sol...

DetexNet: Accurately Diagnosing Frequent and Challenging Pediatric Malignant Tumors.

IEEE transactions on medical imaging
The most frequent extracranial solid tumors of childhood, named peripheral neuroblastic tumors (pNTs), are very challenging to diagnose due to their diversified categories and varying forms. Auxiliary diagnosis methods of such pediatric malignant can...

Wasserstein GANs for MR Imaging: From Paired to Unpaired Training.

IEEE transactions on medical imaging
Lack of ground-truth MR images impedes the common supervised training of neural networks for image reconstruction. To cope with this challenge, this article leverages unpaired adversarial training for reconstruction networks, where the inputs are und...

Improved Deep Learning Based Method for Molecular Similarity Searching Using Stack of Deep Belief Networks.

Molecules (Basel, Switzerland)
Virtual screening (VS) is a computational practice applied in drug discovery research. VS is popularly applied in a computer-based search for new lead molecules based on molecular similarity searching. In chemical databases similarity searching is us...

C-Norm: a neural approach to few-shot entity normalization.

BMC bioinformatics
BACKGROUND: Entity normalization is an important information extraction task which has gained renewed attention in the last decade, particularly in the biomedical and life science domains. In these domains, and more generally in all specialized domai...

The usage of deep neural network improves distinguishing COVID-19 from other suspected viral pneumonia by clinicians on chest CT: a real-world study.

European radiology
OBJECTIVES: Based on the current clinical routine, we aimed to develop a novel deep learning model to distinguish coronavirus disease 2019 (COVID-19) pneumonia from other types of pneumonia and validate it with a real-world dataset (RWD).