AIMC Topic: Humans

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A deep learning approach to the screening of malaria infection: Automated and rapid cell counting, object detection and instance segmentation using Mask R-CNN.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate and early diagnosis is critical to proper malaria treatment and hence death prevention. Several computer vision technologies have emerged in recent years as alternatives to traditional microscopy and rapid diagnostic tests. In this work, we ...

Deep Multi-Magnification Networks for multi-class breast cancer image segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Pathologic analysis of surgical excision specimens for breast carcinoma is important to evaluate the completeness of surgical excision and has implications for future treatment. This analysis is performed manually by pathologists reviewing histologic...

Prediction of future gene expression profile by analyzing its past variation pattern.

Gene expression patterns : GEP
A number of initial Hematopoietic Stem Cells (HSC) are considered in a container that are able to divide into HSCs or differentiate into various types of descendant cells. In this paper, a method is designed to predict an approximate gene expression ...

A Novel Protein Mapping Method for Predicting the Protein Interactions in COVID-19 Disease by Deep Learning.

Interdisciplinary sciences, computational life sciences
The new type of corona virus (SARS-COV-2) emerging in Wuhan, China has spread rapidly to the world and has become a pandemic. In addition to having a significant impact on daily life, it also shows its effect in different areas, including public heal...

Application of Artificial Intelligence for Medical Research.

Biomolecules
The Human Genome Project, completed in 2003 by an international consortium, is considered one of the most important achievements for mankind in the 21st century [...].

Automatic Cephalometric Landmark Identification System Based on the Multi-Stage Convolutional Neural Networks with CBCT Combination Images.

Sensors (Basel, Switzerland)
This study was designed to develop and verify a fully automated cephalometry landmark identification system, based on multi-stage convolutional neural networks (CNNs) architecture, using a combination dataset. In this research, we trained and tested ...

Elbow Motion Trajectory Prediction Using a Multi-Modal Wearable System: A Comparative Analysis of Machine Learning Techniques.

Sensors (Basel, Switzerland)
Motion intention detection is fundamental in the implementation of human-machine interfaces applied to assistive robots. In this paper, multiple machine learning techniques have been explored for creating upper limb motion prediction models, which ge...

Schizotypy in Parkinson's disease predicts dopamine-associated psychosis.

Scientific reports
Psychosis is the most common neuropsychiatric side-effect of dopaminergic therapy in Parkinson's disease (PD). It is still unknown which factors determine individual proneness to psychotic symptoms. Schizotypy is a multifaceted personality trait rela...

Automatic segmentation of ventricular volume by 3D ultrasonography in post haemorrhagic ventricular dilatation among preterm infants.

Scientific reports
To train, evaluate, and validate the application of a deep learning framework in three-dimensional ultrasound (3D US) for the automatic segmentation of ventricular volume in preterm infants with post haemorrhagic ventricular dilatation (PHVD). We tra...

Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass.

Scientific reports
The need for the estimation of the number of microbubbles (MBs) in cardiopulmonary bypass surgery has been recognized among surgeons to avoid postoperative neurological complications. MBs that exceed the diameter of human capillaries may cause endoth...