AIMC Topic: Humans

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Semi-supervised task-driven data augmentation for medical image segmentation.

Medical image analysis
Supervised learning-based segmentation methods typically require a large number of annotated training data to generalize well at test time. In medical applications, curating such datasets is not a favourable option because acquiring a large number of...

Single image super-resolution for whole slide image using convolutional neural networks and self-supervised color normalization.

Medical image analysis
High-quality whole slide scanners used for animal and human pathology scanning are expensive and can produce massive datasets, which limits the access to and adoption of this technique. As a potential solution to these challenges, we present a deep l...

How wide is the application of genetic big data in biomedicine.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
In the era of big data, massive genetic data, as a new industry, has quickly swept almost all industries, especially the pharmaceutical industry. As countries around the world start to build their own gene banks, scientists study the data to explore ...

Immune profile of the tumor microenvironment and the identification of a four-gene signature for lung adenocarcinoma.

Aging
The composition and relative abundances of immune cells in the tumor microenvironment are key factors affecting the progression of lung adenocarcinomas (LUADs) and the efficacy of immunotherapy. Using the cancer gene expression dataset from The Cance...

A novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs.

Scientific reports
Fluorescein angiography (FA) is a procedure used to image the vascular structure of the retina and requires the insertion of an exogenous dye with potential adverse side effects. Currently, there is only one alternative non-invasive system based on O...

Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system.

Scientific reports
Timely and effective clinical decision-making for COVID-19 requires rapid identification of risk factors for disease outcomes. Our objective was to identify characteristics available immediately upon first clinical evaluation related COVID-19 mortali...

Precision medicine in the era of artificial intelligence: implications in chronic disease management.

Journal of translational medicine
Aberrant metabolism is the root cause of several serious health issues, creating a huge burden to health and leading to diminished life expectancy. A dysregulated metabolism induces the secretion of several molecules which in turn trigger the inflamm...

Convolutional neural network for automated mass segmentation in mammography.

BMC bioinformatics
BACKGROUND: Automatic segmentation and localization of lesions in mammogram (MG) images are challenging even with employing advanced methods such as deep learning (DL) methods. We developed a new model based on the architecture of the semantic segmen...

A plea to merge clinical and public health practices: reasons and consequences.

BMC health services research
BACKGROUND: Revisiting professionalism, both as a medical ideal and educational topic, this paper asks whether, in the rise of artificial intelligence, healthcare commoditisation and environmental challenges, a rationale exists for merging clinical a...

DeepRetina: Layer Segmentation of Retina in OCT Images Using Deep Learning.

Translational vision science & technology
PURPOSE: To automate the segmentation of retinal layers, we propose DeepRetina, a method based on deep neural networks.