AIMC Topic: Humans

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COVID-19 image classification using deep learning: Advances, challenges and opportunities.

Computers in biology and medicine
Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) ima...

INASNET: Automatic identification of coronavirus disease (COVID-19) based on chest X-ray using deep neural network.

ISA transactions
Testing is one of the important methodologies used by various countries in order to fight against COVID-19 infection. The infection is considered as one of the deadliest ones although the mortality rate is not very high. COVID-19 infection is being c...

Building artificial intelligence and machine learning models : a primer for emergency physicians.

Emergency medicine journal : EMJ
There has been a rise in the number of studies relating to the role of artificial intelligence (AI) in healthcare. Its potential in Emergency Medicine (EM) has been explored in recent years with operational, predictive, diagnostic and prognostic emer...

[Robotics in nursing-opportunties and limits].

Der Hautarzt; Zeitschrift fur Dermatologie, Venerologie, und verwandte Gebiete

Robot-Assisted vs Laparoscopic Right Hemicolectomy in Octogenarians.

Journal of the American Medical Directors Association
OBJECTIVE: With increasing age, there is greater need for right-sided colonic resections than its left-sided counterparts. Older age is associated with limited physical and functional status, which carries greater operative risk. Improvements in robo...

Postsurgical complications after robot-assisted transaxillary thyroidectomy: critical analysis of a large cohort of European patients.

Updates in surgery
In the last decade, robot-assisted trans-axillary thyroidectomy has spread rapidly and has been proven to be a safe and effective procedure. However, several case series have reported new complications that have led to criticism regarding this approa...

Deep learning-based simultaneous registration and unsupervised non-correspondence segmentation of medical images with pathologies.

International journal of computer assisted radiology and surgery
PURPOSE: The registration of medical images often suffers from missing correspondences due to inter-patient variations, pathologies and their progression leading to implausible deformations that cause misregistrations and might eliminate valuable inf...

A real use case of semi-supervised learning for mammogram classification in a local clinic of Costa Rica.

Medical & biological engineering & computing
The implementation of deep learning-based computer-aided diagnosis systems for the classification of mammogram images can help in improving the accuracy, reliability, and cost of diagnosing patients. However, training a deep learning model requires a...

Artificial intelligence for histological subtype classification of breast cancer: combining multi-scale feature maps and the recurrent attention model.

Histopathology
AIMS: The aim of this study was to apply a two-stage deep model combining multi-scale feature maps and the recurrent attention model (RAM) to assist with the pathological diagnosis of breast cancer histological subtypes by the use of whole slide imag...